Object Detection(目标检测神文)

男娘i 2022-05-16 13:18 521阅读 0赞

目标检测神文，非常全而且持续在更新。转发自：[https://handong1587.github.io/deep\_learning/2015/10/09/object-detection.html][https_handong1587.github.io_deep_learning_2015_10_09_object-detection.html]，如有侵权联系删除。  
更新时间：  
20190226 暂时停更  
不再更新，最新检测文章请移步：https://blog.csdn.net/hw5226349/article/details/88733364  
我会跟进原作者博客持续更新，加入自己对目标检测领域的一些新研究及论文解读。博客根据需求直接进行关键字搜索，例如2018，可找到最新论文。

### 文章目录 ###

*  Papers
 *   *  损失函数
     *   *  \[CVPR2019\] Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression
         *  Deep Neural Networks for Object Detection
         *  OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks
     *  R-CNN
     *   *  Rich feature hierarchies for accurate object detection and semantic segmentation
     *  Fast R-CNN
     *   *  Fast R-CNN
         *  A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detection
     *  Faster R-CNN
     *   *  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
         *  R-CNN minus R
         *  Faster R-CNN in MXNet with distributed implementation and data parallelization
         *  Contextual Priming and Feedback for Faster R-CNN
         *  An Implementation of Faster RCNN with Study for Region Sampling
         *  Interpretable R-CNN
         *  \[AAAI2019\]Object Detection based on Region Decomposition and Assembly
     *  Light-Head R-CNN
     *   *  Light-Head R-CNN: In Defense of Two-Stage Object Detector
         *  Cascade R-CNN: Delving into High Quality Object Detection
     *  MultiBox
     *   *  Scalable Object Detection using Deep Neural Networks
         *  Scalable, High-Quality Object Detection
     *  SPP-Net
     *   *  Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
         *  DeepID-Net: Deformable Deep Convolutional Neural Networks for Object Detection
         *  Object Detectors Emerge in Deep Scene CNNs
         *  segDeepM: Exploiting Segmentation and Context in Deep Neural Networks for Object Detection
         *  Object Detection Networks on Convolutional Feature Maps
         *  Improving Object Detection with Deep Convolutional Networks via Bayesian Optimization and Structured Prediction
         *  DeepBox: Learning Objectness with Convolutional Networks
     *  MR-CNN
     *   *  Object detection via a multi-region & semantic segmentation-aware CNN model
     *  YOLO
     *   *  You Only Look Once: Unified, Real-Time Object Detection
         *  darkflow - translate darknet to tensorflow. Load trained weights, retrain/fine-tune them using tensorflow, export constant graph def to C++
         *  Start Training YOLO with Our Own Data
         *  YOLO: Core ML versus MPSNNGraph
         *  TensorFlow YOLO object detection on Android
         *  Computer Vision in iOS – Object Detection
     *  YOLOv2
     *   *  YOLO9000: Better, Faster, Stronger
         *  darknet\_scripts
         *  Yolo\_mark: GUI for marking bounded boxes of objects in images for training Yolo v2
         *  LightNet: Bringing pjreddie’s DarkNet out of the shadows
         *  YOLO v2 Bounding Box Tool
     *  YOLOv3
     *   *  YOLOv3: An Incremental Improvement
         *  YOLO-LITE: A Real-Time Object Detection Algorithm Optimized for Non-GPU Computers
         *  AttentionNet: Aggregating Weak Directions for Accurate Object Detection
     *  DenseBox
     *   *  DenseBox: Unifying Landmark Localization with End to End Object Detection
     *  SSD
     *   *  SSD: Single Shot MultiBox Detector
     *  DSSD
     *   *  DSSD : Deconvolutional Single Shot Detector
         *  Enhancement of SSD by concatenating feature maps for object detection
         *  Context-aware Single-Shot Detector
         *  Feature-Fused SSD: Fast Detection for Small Objects
     *  FSSD
     *   *  FSSD: Feature Fusion Single Shot Multibox Detector
         *  Weaving Multi-scale Context for Single Shot Detector
     *  ESSD
     *   *  Extend the shallow part of Single Shot MultiBox Detector via Convolutional Neural Network
         *  Tiny SSD: A Tiny Single-shot Detection Deep Convolutional Neural Network for Real-time Embedded Object Detection
         *  MDSSD: Multi-scale Deconvolutional Single Shot Detector for small objects
     *  Inside-Outside Net (ION)
     *   *  Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks
         *  Adaptive Object Detection Using Adjacency and Zoom Prediction
         *  G-CNN: an Iterative Grid Based Object Detector
     *  Factors in Finetuning Deep Model for object detection
     *   *  Factors in Finetuning Deep Model for Object Detection with Long-tail Distribution
         *  We don’t need no bounding-boxes: Training object class detectors using only human verification
         *  HyperNet: Towards Accurate Region Proposal Generation and Joint Object Detection
         *  A MultiPath Network for Object Detection
     *  CRAFT
     *   *  CRAFT Objects from Images
     *  OHEM
     *   *  Training Region-based Object Detectors with Online Hard Example Mining
         *  S-OHEM: Stratified Online Hard Example Mining for Object Detection
         *  Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers
     *  R-FCN
     *   *  R-FCN: Object Detection via Region-based Fully Convolutional Networks
         *  R-FCN-3000 at 30fps: Decoupling Detection and Classification
         *  Recycle deep features for better object detection
     *  MS-CNN
     *   *  A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection
         *  Multi-stage Object Detection with Group Recursive Learning
         *  Subcategory-aware Convolutional Neural Networks for Object Proposals and Detection
     *  PVANET
     *   *  PVANet: Lightweight Deep Neural Networks for Real-time Object Detection
     *  GBD-Net
     *   *  Gated Bi-directional CNN for Object Detection
         *  Crafting GBD-Net for Object Detection
         *  StuffNet: Using ‘Stuff’ to Improve Object Detection
         *  Generalized Haar Filter based Deep Networks for Real-Time Object Detection in Traffic Scene
         *  Hierarchical Object Detection with Deep Reinforcement Learning
         *  Learning to detect and localize many objects from few examples
         *  Speed/accuracy trade-offs for modern convolutional object detectors
         *  SqueezeDet: Unified, Small, Low Power Fully Convolutional Neural Networks for Real-Time Object Detection for Autonomous Driving
     *  Feature Pyramid Network (FPN)
     *   *  Feature Pyramid Networks for Object Detection
         *  Action-Driven Object Detection with Top-Down Visual Attentions
         *  Beyond Skip Connections: Top-Down Modulation for Object Detection
         *  Wide-Residual-Inception Networks for Real-time Object Detection
         *  Attentional Network for Visual Object Detection
         *  Learning Chained Deep Features and Classifiers for Cascade in Object Detection
         *  DeNet: Scalable Real-time Object Detection with Directed Sparse Sampling
         *  Discriminative Bimodal Networks for Visual Localization and Detection with Natural Language Queries
         *  Spatial Memory for Context Reasoning in Object Detection
         *  Accurate Single Stage Detector Using Recurrent Rolling Convolution
         *  Deep Occlusion Reasoning for Multi-Camera Multi-Target Detection
         *  LCDet: Low-Complexity Fully-Convolutional Neural Networks for Object Detection in Embedded Systems
         *  Point Linking Network for Object Detection
         *  Perceptual Generative Adversarial Networks for Small Object Detection
         *  Few-shot Object Detection
         *  Yes-Net: An effective Detector Based on Global Information
         *  SMC Faster R-CNN: Toward a scene-specialized multi-object detector
         *  Towards lightweight convolutional neural networks for object detection
         *  RON: Reverse Connection with Objectness Prior Networks for Object Detection
         *  Mimicking Very Efficient Network for Object Detection
         *  Residual Features and Unified Prediction Network for Single Stage Detection
         *  Deformable Part-based Fully Convolutional Network for Object Detection
         *  Adaptive Feeding: Achieving Fast and Accurate Detections by Adaptively Combining Object Detectors
         *  Recurrent Scale Approximation for Object Detection in CNN
     *  DSOD
     *   *  DSOD: Learning Deeply Supervised Object Detectors from Scratch
         *  Object Detection from Scratch with Deep Supervision
         *  Focal Loss for Dense Object Detection
         *  Focal Loss Dense Detector for Vehicle Surveillance
         *  CoupleNet: Coupling Global Structure with Local Parts for Object Detection
         *  Incremental Learning of Object Detectors without Catastrophic Forgetting
         *  Zoom Out-and-In Network with Map Attention Decision for Region Proposal and Object Detection
         *  StairNet: Top-Down Semantic Aggregation for Accurate One Shot Detection
         *  Dynamic Zoom-in Network for Fast Object Detection in Large Images
         *  Zero-Annotation Object Detection with Web Knowledge Transfer
     *  MegDet
     *   *  MegDet: A Large Mini-Batch Object Detector
         *  Single-Shot Refinement Neural Network for Object Detection
         *  Receptive Field Block Net for Accurate and Fast Object Detection
         *  An Analysis of Scale Invariance in Object Detection - SNIP
         *  Feature Selective Networks for Object Detection
         *  Learning a Rotation Invariant Detector with Rotatable Bounding Box
         *  Scalable Object Detection for Stylized Objects
         *  Learning Object Detectors from Scratch with Gated Recurrent Feature Pyramids
         *  Deep Regionlets for Object Detection
         *  Training and Testing Object Detectors with Virtual Images
         *  Large-Scale Object Discovery and Detector Adaptation from Unlabeled Video
         *  Spot the Difference by Object Detection
         *  Localization-Aware Active Learning for Object Detection
         *  Object Detection with Mask-based Feature Encoding
         *  LSTD: A Low-Shot Transfer Detector for Object Detection
         *  Domain Adaptive Faster R-CNN for Object Detection in the Wild
         *  Pseudo Mask Augmented Object Detection
         *  Revisiting RCNN: On Awakening the Classification Power of Faster RCNN
         *  Decoupled Classification Refinement: Hard False Positive Suppression for Object Detection
         *  Learning Region Features for Object Detection
         *  Single-Shot Bidirectional Pyramid Networks for High-Quality Object Detection
         *  Object Detection for Comics using Manga109 Annotations
         *  Task-Driven Super Resolution: Object Detection in Low-resolution Images
         *  Transferring Common-Sense Knowledge for Object Detection
         *  Multi-scale Location-aware Kernel Representation for Object Detection
         *  Loss Rank Mining: A General Hard Example Mining Method for Real-time Detectors
         *  DetNet: A Backbone network for Object Detection
         *  Robust Physical Adversarial Attack on Faster R-CNN Object Detector
         *  AdvDetPatch: Attacking Object Detectors with Adversarial Patches
         *  Attacking Object Detectors via Imperceptible Patches on Background
         *  Physical Adversarial Examples for Object Detectors
         *  Quantization Mimic: Towards Very Tiny CNN for Object Detection
         *  Object detection at 200 Frames Per Second
         *  Object Detection using Domain Randomization and Generative Adversarial Refinement of Synthetic Images
         *  SNIPER: Efficient Multi-Scale Training
         *  Soft Sampling for Robust Object Detection
         *  MetaAnchor: Learning to Detect Objects with Customized Anchors
         *  Localization Recall Precision (LRP): A New Performance Metric for Object Detection
         *  Auto-Context R-CNN
         *  Pooling Pyramid Network for Object Detection
         *  Modeling Visual Context is Key to Augmenting Object Detection Datasets
         *  Dual Refinement Network for Single-Shot Object Detection
         *  Acquisition of Localization Confidence for Accurate Object Detection
         *  CornerNet: Detecting Objects as Paired Keypoints
         *  Unsupervised Hard Example Mining from Videos for Improved Object Detection
         *  SAN: Learning Relationship between Convolutional Features for Multi-Scale Object Detection
         *  A Survey of Modern Object Detection Literature using Deep Learning
         *  Tiny-DSOD: Lightweight Object Detection for Resource-Restricted Usages
         *  Deep Feature Pyramid Reconfiguration for Object Detection
         *  MDCN: Multi-Scale, Deep Inception Convolutional Neural Networks for Efficient Object Detection
         *  Recent Advances in Object Detection in the Age of Deep Convolutional Neural Networks
         *  Deep Learning for Generic Object Detection: A Survey
         *  Training Confidence-Calibrated Classifier for Detecting Out-of-Distribution Samples
         *  ScratchDet:Exploring to Train Single-Shot Object Detectors from Scratch
         *  Fast and accurate object detection in high resolution 4K and 8K video using GPUs
         *  Hybrid Knowledge Routed Modules for Large-scale Object Detection
         *  Gradient Harmonized Single-stage Detector
         *  M2Det: A Single-Shot Object Detector based on Multi-Level Feature Pyramid Network
         *  BAN: Focusing on Boundary Context for Object Detection
         *  Multi-layer Pruning Framework for Compressing Single Shot MultiBox Detector
         *  R2CNN++: Multi-Dimensional Attention Based Rotation Invariant Detector with Robust Anchor Strategy
         *  DeRPN: Taking a further step toward more general object detection
         *  Fast Efficient Object Detection Using Selective Attention
         *  Sampling Techniques for Large-Scale Object Detection from Sparsely Annotated Objects
         *  Efficient Coarse-to-Fine Non-Local Module for the Detection of Small Objects
         *  Deep Regionlets: Blended Representation and Deep Learning for Generic Object Detection
         *  Grid R-CNN
         *  Transferable Adversarial Attacks for Image and Video Object Detection
         *  Anchor Box Optimization for Object Detection
         *  AutoFocus: Efficient Multi-Scale Inference
         *  Practical Adversarial Attack Against Object Detector
         *  Learning Efficient Detector with Semi-supervised Adaptive Distillation
         *  Scale-Aware Trident Networks for Object Detection
         *  Region Proposal by Guided Anchoring
         *  Consistent Optimization for Single-Shot Object Detection
         *  Bottom-up Object Detection by Grouping Extreme and Center Points
         *  A Single-shot Object Detector with Feature Aggragation and Enhancement
         *  Bag of Freebies for Training Object Detection Neural Networks
     *  Non-Maximum Suppression (NMS)
     *   *  End-to-End Integration of a Convolutional Network, Deformable Parts Model and Non-Maximum Suppression
         *  A convnet for non-maximum suppression
         *  Soft-NMS – Improving Object Detection With One Line of Code
         *  Learning non-maximum suppression
         *  Relation Networks for Object Detection
         *  Learning Pairwise Relationship for Multi-object Detection in Crowded Scenes
         *  Daedalus: Breaking Non-Maximum Suppression in Object Detection via Adversarial Examples
     *  Adversarial Examples
     *   *  Adversarial Examples that Fool Detectors
         *  Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods
     *  Weakly Supervised Object Detection
     *   *  Track and Transfer: Watching Videos to Simulate Strong Human Supervision for Weakly-Supervised Object Detection
         *  Weakly supervised object detection using pseudo-strong labels
         *  Saliency Guided End-to-End Learning for Weakly Supervised Object Detection
         *  Visual and Semantic Knowledge Transfer for Large Scale Semi-supervised Object Detection
     *  Video Object Detection
     *   *  Learning Object Class Detectors from Weakly Annotated Video
         *  Analysing domain shift factors between videos and images for object detection
         *  Video Object Recognition
         *  Deep Learning for Saliency Prediction in Natural Video
         *  T-CNN: Tubelets with Convolutional Neural Networks for Object Detection from Videos
         *  Object Detection from Video Tubelets with Convolutional Neural Networks
         *  Object Detection in Videos with Tubelets and Multi-context Cues
         *  Context Matters: Refining Object Detection in Video with Recurrent Neural Networks
         *  CNN Based Object Detection in Large Video Images
         *  Object Detection in Videos with Tubelet Proposal Networks
         *  Flow-Guided Feature Aggregation for Video Object Detection
         *  Video Object Detection using Faster R-CNN
         *  Improving Context Modeling for Video Object Detection and Tracking
         *  Temporal Dynamic Graph LSTM for Action-driven Video Object Detection
         *  Mobile Video Object Detection with Temporally-Aware Feature Maps
         *  Towards High Performance Video Object Detection
         *  Impression Network for Video Object Detection
         *  Spatial-Temporal Memory Networks for Video Object Detection
         *  3D-DETNet: a Single Stage Video-Based Vehicle Detector
         *  Object Detection in Videos by Short and Long Range Object Linking
         *  Object Detection in Video with Spatiotemporal Sampling Networks
         *  Towards High Performance Video Object Detection for Mobiles
         *  Optimizing Video Object Detection via a Scale-Time Lattice
         *  Pack and Detect: Fast Object Detection in Videos Using Region-of-Interest Packing
         *  Fast Object Detection in Compressed Video
         *  Tube-CNN: Modeling temporal evolution of appearance for object detection in video
         *  AdaScale: Towards Real-time Video Object Detection Using Adaptive Scaling
     *  Object Detection on Mobile Devices
     *   *  Pelee: A Real-Time Object Detection System on Mobile Devices
     *  Object Detection in 3D
     *   *  Vote3Deep: Fast Object Detection in 3D Point Clouds Using Efficient Convolutional Neural Networks
         *  Complex-YOLO: Real-time 3D Object Detection on Point Clouds
         *  Focal Loss in 3D Object Detection
         *  3D Object Detection Using Scale Invariant and Feature Reweighting Networks
         *  3D Backbone Network for 3D Object Detection
     *  Object Detection on RGB-D
     *   *  Learning Rich Features from RGB-D Images for Object Detection and Segmentation
         *  Differential Geometry Boosts Convolutional Neural Networks for Object Detection
         *  A Self-supervised Learning System for Object Detection using Physics Simulation and Multi-view Pose Estimation
     *  Zero-Shot Object Detection
     *   *  Zero-Shot Detection
         *  Zero-Shot Object Detection
         *  Zero-Shot Object Detection: Learning to Simultaneously Recognize and Localize Novel Concepts
         *  Zero-Shot Object Detection by Hybrid Region Embedding
     *  Salient Object Detection
     *   *  Best Deep Saliency Detection Models (CVPR 2016 & 2015)
         *  Large-scale optimization of hierarchical features for saliency prediction in natural images
         *  Predicting Eye Fixations using Convolutional Neural Networks
         *  Saliency Detection by Multi-Context Deep Learning
         *  DeepSaliency: Multi-Task Deep Neural Network Model for Salient Object Detection
         *  SuperCNN: A Superpixelwise Convolutional Neural Network for Salient Object Detection
         *  Shallow and Deep Convolutional Networks for Saliency Prediction
         *  Recurrent Attentional Networks for Saliency Detection
         *  Two-Stream Convolutional Networks for Dynamic Saliency Prediction
     *  Unconstrained Salient Object Detection
     *   *  Unconstrained Salient Object Detection via Proposal Subset Optimization
         *  DHSNet: Deep Hierarchical Saliency Network for Salient Object Detection
         *  Salient Object Subitizing
         *  Deeply-Supervised Recurrent Convolutional Neural Network for Saliency Detection
         *  Saliency Detection via Combining Region-Level and Pixel-Level Predictions with CNNs
         *  Edge Preserving and Multi-Scale Contextual Neural Network for Salient Object Detection
         *  A Deep Multi-Level Network for Saliency Prediction
         *  Visual Saliency Detection Based on Multiscale Deep CNN Features
         *  A Deep Spatial Contextual Long-term Recurrent Convolutional Network for Saliency Detection
         *  Deeply supervised salient object detection with short connections
         *  Weakly Supervised Top-down Salient Object Detection
         *  SalGAN: Visual Saliency Prediction with Generative Adversarial Networks
         *  Visual Saliency Prediction Using a Mixture of Deep Neural Networks
         *  A Fast and Compact Salient Score Regression Network Based on Fully Convolutional Network
         *  Saliency Detection by Forward and Backward Cues in Deep-CNNs
         *  Supervised Adversarial Networks for Image Saliency Detection
         *  Group-wise Deep Co-saliency Detection
         *  Towards the Success Rate of One: Real-time Unconstrained Salient Object Detection
         *  Amulet: Aggregating Multi-level Convolutional Features for Salient Object Detection
         *  Learning Uncertain Convolutional Features for Accurate Saliency Detection
         *  Deep Edge-Aware Saliency Detection
         *  Self-explanatory Deep Salient Object Detection
         *  PiCANet: Learning Pixel-wise Contextual Attention in ConvNets and Its Application in Saliency Detection
         *  DeepFeat: A Bottom Up and Top Down Saliency Model Based on Deep Features of Convolutional Neural Nets
         *  Recurrently Aggregating Deep Features for Salient Object Detection
         *  Deep saliency: What is learnt by a deep network about saliency?
         *  Contrast-Oriented Deep Neural Networks for Salient Object Detection
         *  Salient Object Detection by Lossless Feature Reflection
         *  HyperFusion-Net: Densely Reflective Fusion for Salient Object Detection
     *  Video Saliency Detection
     *   *  Deep Learning For Video Saliency Detection
         *  Video Salient Object Detection Using Spatiotemporal Deep Features
         *  Predicting Video Saliency with Object-to-Motion CNN and Two-layer Convolutional LSTM
     *  Visual Relationship Detection
     *   *  Visual Relationship Detection with Language Priors
         *  ViP-CNN: A Visual Phrase Reasoning Convolutional Neural Network for Visual Relationship Detection
         *  Visual Translation Embedding Network for Visual Relation Detection
         *  Deep Variation-structured Reinforcement Learning for Visual Relationship and Attribute Detection
         *  Detecting Visual Relationships with Deep Relational Networks
         *  Identifying Spatial Relations in Images using Convolutional Neural Networks
         *  PPR-FCN: Weakly Supervised Visual Relation Detection via Parallel Pairwise R-FCN
         *  Natural Language Guided Visual Relationship Detection
         *  Detecting Visual Relationships Using Box Attention
         *  Google AI Open Images - Visual Relationship Track
         *  Context-Dependent Diffusion Network for Visual Relationship Detection
         *  A Problem Reduction Approach for Visual Relationships Detection
     *  Face Deteciton
     *   *  Multi-view Face Detection Using Deep Convolutional Neural Networks
         *  From Facial Parts Responses to Face Detection: A Deep Learning Approach
         *  Compact Convolutional Neural Network Cascade for Face Detection
         *  Face Detection with End-to-End Integration of a ConvNet and a 3D Model
         *  CMS-RCNN: Contextual Multi-Scale Region-based CNN for Unconstrained Face Detection
         *  Towards a Deep Learning Framework for Unconstrained Face Detection
         *  Supervised Transformer Network for Efficient Face Detection
         *  UnitBox: An Advanced Object Detection Network
         *  Bootstrapping Face Detection with Hard Negative Examples
         *  Grid Loss: Detecting Occluded Faces
         *  A Multi-Scale Cascade Fully Convolutional Network Face Detector
     *  MTCNN
     *   *  Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Neural Networks
         *  Face Detection using Deep Learning: An Improved Faster RCNN Approach
         *  Faceness-Net: Face Detection through Deep Facial Part Responses
         *  Multi-Path Region-Based Convolutional Neural Network for Accurate Detection of Unconstrained “Hard Faces”
         *  End-To-End Face Detection and Recognition
         *  Face R-CNN
         *  Face Detection through Scale-Friendly Deep Convolutional Networks
         *  Scale-Aware Face Detection
         *  Detecting Faces Using Inside Cascaded Contextual CNN
         *  Multi-Branch Fully Convolutional Network for Face Detection
         *  SSH: Single Stage Headless Face Detector
         *  Dockerface: an easy to install and use Faster R-CNN face detector in a Docker container
         *  FaceBoxes: A CPU Real-time Face Detector with High Accuracy
         *  S3FD: Single Shot Scale-invariant Face Detector
         *  Detecting Faces Using Region-based Fully Convolutional Networks
         *  AffordanceNet: An End-to-End Deep Learning Approach for Object Affordance Detection
         *  Face Attention Network: An effective Face Detector for the Occluded Faces
         *  Feature Agglomeration Networks for Single Stage Face Detection
         *  Face Detection Using Improved Faster RCNN
         *  PyramidBox: A Context-assisted Single Shot Face Detector
         *  A Fast Face Detection Method via Convolutional Neural Network
         *  Beyond Trade-off: Accelerate FCN-based Face Detector with Higher Accuracy
         *  Real-Time Rotation-Invariant Face Detection with Progressive Calibration Networks
         *  SFace: An Efficient Network for Face Detection in Large Scale Variations
         *  Survey of Face Detection on Low-quality Images
         *  Anchor Cascade for Efficient Face Detection
         *  Adversarial Attacks on Face Detectors using Neural Net based Constrained Optimization
         *  Selective Refinement Network for High Performance Face Detection
         *  DSFD: Dual Shot Face Detector
         *  Learning Better Features for Face Detection with Feature Fusion and Segmentation Supervision
         *  FA-RPN: Floating Region Proposals for Face Detection
         *  Robust and High Performance Face Detector
         *  DAFE-FD: Density Aware Feature Enrichment for Face Detection
         *  Improved Selective Refinement Network for Face Detection
         *  Revisiting a single-stage method for face detection
     *  Detect Small Faces
     *   *  Finding Tiny Faces
         *  Detecting and counting tiny faces
         *  Seeing Small Faces from Robust Anchor’s Perspective
         *  Face-MagNet: Magnifying Feature Maps to Detect Small Faces
         *  Robust Face Detection via Learning Small Faces on Hard Images
         *  SFA: Small Faces Attention Face Detector
     *  Person Head Detection
     *   *  Context-aware CNNs for person head detection
         *  Detecting Heads using Feature Refine Net and Cascaded Multi-scale Architecture
         *  A Comparison of CNN-based Face and Head Detectors for Real-Time Video Surveillance Applications
         *  FCHD: A fast and accurate head detector
     *  Pedestrian Detection / People Detection
     *   *  Pedestrian Detection aided by Deep Learning Semantic Tasks
         *  Deep Learning Strong Parts for Pedestrian Detection
         *  Taking a Deeper Look at Pedestrians
         *  Convolutional Channel Features
         *  End-to-end people detection in crowded scenes
         *  Learning Complexity-Aware Cascades for Deep Pedestrian Detection
         *  Deep convolutional neural networks for pedestrian detection
         *  Scale-aware Fast R-CNN for Pedestrian Detection
         *  New algorithm improves speed and accuracy of pedestrian detection
         *  Pushing the Limits of Deep CNNs for Pedestrian Detection
         *  A Real-Time Deep Learning Pedestrian Detector for Robot Navigation
         *  A Real-Time Pedestrian Detector using Deep Learning for Human-Aware Navigation
         *  Is Faster R-CNN Doing Well for Pedestrian Detection?
         *  Unsupervised Deep Domain Adaptation for Pedestrian Detection
         *  Reduced Memory Region Based Deep Convolutional Neural Network Detection
         *  Fused DNN: A deep neural network fusion approach to fast and robust pedestrian detection
         *  Detecting People in Artwork with CNNs
         *  Multispectral Deep Neural Networks for Pedestrian Detection
         *  Box-level Segmentation Supervised Deep Neural Networks for Accurate and Real-time Multispectral Pedestrian Detection
         *  Deep Multi-camera People Detection
         *  Expecting the Unexpected: Training Detectors for Unusual Pedestrians with Adversarial Imposters
         *  What Can Help Pedestrian Detection?
         *  Illuminating Pedestrians via Simultaneous Detection & Segmentation
         *  Rotational Rectification Network for Robust Pedestrian Detection
         *  STD-PD: Generating Synthetic Training Data for Pedestrian Detection in Unannotated Videos
         *  Too Far to See? Not Really! — Pedestrian Detection with Scale-aware Localization Policy
         *  Repulsion Loss: Detecting Pedestrians in a Crowd
         *  Aggregated Channels Network for Real-Time Pedestrian Detection
         *  Illumination-aware Faster R-CNN for Robust Multispectral Pedestrian Detection
         *  Exploring Multi-Branch and High-Level Semantic Networks for Improving Pedestrian Detection
         *  Pedestrian-Synthesis-GAN: Generating Pedestrian Data in Real Scene and Beyond
         *  PCN: Part and Context Information for Pedestrian Detection with CNNs
         *  Small-scale Pedestrian Detection Based on Somatic Topology Localization and Temporal Feature Aggregation
         *  Occlusion-aware R-CNN: Detecting Pedestrians in a Crowd
         *  Multispectral Pedestrian Detection via Simultaneous Detection and Segmentation
         *  Pedestrian Detection with Autoregressive Network Phases
         *  The Cross-Modality Disparity Problem in Multispectral Pedestrian Detection
     *  Vehicle Detection
     *   *  DAVE: A Unified Framework for Fast Vehicle Detection and Annotation
         *  Evolving Boxes for fast Vehicle Detection
         *  Fine-Grained Car Detection for Visual Census Estimation
         *  SINet: A Scale-insensitive Convolutional Neural Network for Fast Vehicle Detection
         *  Label and Sample: Efficient Training of Vehicle Object Detector from Sparsely Labeled Data
         *  Domain Randomization for Scene-Specific Car Detection and Pose Estimation
         *  ShuffleDet: Real-Time Vehicle Detection Network in On-board Embedded UAV Imagery
     *  Traffic-Sign Detection
     *   *  Traffic-Sign Detection and Classification in the Wild
         *  Evaluating State-of-the-art Object Detector on Challenging Traffic Light Data
         *  Detecting Small Signs from Large Images
         *  Localized Traffic Sign Detection with Multi-scale Deconvolution Networks
         *  Detecting Traffic Lights by Single Shot Detection
         *  A Hierarchical Deep Architecture and Mini-Batch Selection Method For Joint Traffic Sign and Light Detection
     *  Skeleton Detection
     *   *  Object Skeleton Extraction in Natural Images by Fusing Scale-associated Deep Side Outputs
         *  DeepSkeleton: Learning Multi-task Scale-associated Deep Side Outputs for Object Skeleton Extraction in Natural Images
         *  SRN: Side-output Residual Network for Object Symmetry Detection in the Wild
         *  Hi-Fi: Hierarchical Feature Integration for Skeleton Detection
     *  Fruit Detection
     *   *  Deep Fruit Detection in Orchards
         *  Image Segmentation for Fruit Detection and Yield Estimation in Apple Orchards
     *  Shadow Detection
     *   *  Fast Shadow Detection from a Single Image Using a Patched Convolutional Neural Network
         *  A+D-Net: Shadow Detection with Adversarial Shadow Attenuation
         *  Stacked Conditional Generative Adversarial Networks for Jointly Learning Shadow Detection and Shadow Removal
         *  Direction-aware Spatial Context Features for Shadow Detection
         *  Direction-aware Spatial Context Features for Shadow Detection and Removal
     *  Others Detection
     *   *  Deep Deformation Network for Object Landmark Localization
         *  Fashion Landmark Detection in the Wild
         *  Deep Learning for Fast and Accurate Fashion Item Detection
         *  OSMDeepOD - OSM and Deep Learning based Object Detection from Aerial Imagery (formerly known as “OSM-Crosswalk-Detection”)
         *  Selfie Detection by Synergy-Constraint Based Convolutional Neural Network
         *  Associative Embedding:End-to-End Learning for Joint Detection and Grouping
         *  Deep Cuboid Detection: Beyond 2D Bounding Boxes
         *  Automatic Model Based Dataset Generation for Fast and Accurate Crop and Weeds Detection
         *  Deep Learning Logo Detection with Data Expansion by Synthesising Context
         *  Scalable Deep Learning Logo Detection
         *  Pixel-wise Ear Detection with Convolutional Encoder-Decoder Networks
         *  Automatic Handgun Detection Alarm in Videos Using Deep Learning
         *  Objects as context for part detection
         *  Using Deep Networks for Drone Detection
         *  Cut, Paste and Learn: Surprisingly Easy Synthesis for Instance Detection
         *  Target Driven Instance Detection
         *  DeepVoting: An Explainable Framework for Semantic Part Detection under Partial Occlusion
         *  VPGNet: Vanishing Point Guided Network for Lane and Road Marking Detection and Recognition
         *  Grab, Pay and Eat: Semantic Food Detection for Smart Restaurants
         *  ReMotENet: Efficient Relevant Motion Event Detection for Large-scale Home Surveillance Videos
         *  Deep Learning Object Detection Methods for Ecological Camera Trap Data
         *  EL-GAN: Embedding Loss Driven Generative Adversarial Networks for Lane Detection
         *  Towards End-to-End Lane Detection: an Instance Segmentation Approach
         *  iCAN: Instance-Centric Attention Network for Human-Object Interaction Detection
         *  Densely Supervised Grasp Detector (DSGD)
     *  Object Proposal
     *   *  DeepProposal: Hunting Objects by Cascading Deep Convolutional Layers
         *  Scale-aware Pixel-wise Object Proposal Networks
         *  Attend Refine Repeat: Active Box Proposal Generation via In-Out Localization
         *  Learning to Segment Object Proposals via Recursive Neural Networks
         *  Learning Detection with Diverse Proposals
         *  ScaleNet: Guiding Object Proposal Generation in Supermarkets and Beyond
         *  Improving Small Object Proposals for Company Logo Detection
         *  Open Logo Detection Challenge
         *  AttentionMask: Attentive, Efficient Object Proposal Generation Focusing on Small Objects
     *  Localization
     *   *  Beyond Bounding Boxes: Precise Localization of Objects in Images
         *  Weakly Supervised Object Localization with Multi-fold Multiple Instance Learning
         *  Weakly Supervised Object Localization Using Size Estimates
         *  Active Object Localization with Deep Reinforcement Learning
         *  Localizing objects using referring expressions
         *  LocNet: Improving Localization Accuracy for Object Detection
         *  Learning Deep Features for Discriminative Localization
         *  ContextLocNet: Context-Aware Deep Network Models for Weakly Supervised Localization
         *  Ensemble of Part Detectors for Simultaneous Classification and Localization
         *  STNet: Selective Tuning of Convolutional Networks for Object Localization
         *  Soft Proposal Networks for Weakly Supervised Object Localization
         *  Fine-grained Discriminative Localization via Saliency-guided Faster R-CNN
     *  Tutorials / Talks
     *   *  Convolutional Feature Maps: Elements of efficient (and accurate) CNN-based object detection
         *  Towards Good Practices for Recognition & Detection
         *  Work in progress: Improving object detection and instance segmentation for small objects
         *  Object Detection with Deep Learning: A Review
     *  Projects
     *   *  Detectron
         *  TensorBox: a simple framework for training neural networks to detect objects in images
         *  Object detection in torch: Implementation of some object detection frameworks in torch
         *  Using DIGITS to train an Object Detection network
         *  FCN-MultiBox Detector
         *  KittiBox: A car detection model implemented in Tensorflow.
         *  Deformable Convolutional Networks + MST + Soft-NMS
         *  How to Build a Real-time Hand-Detector using Neural Networks (SSD) on Tensorflow
         *  Metrics for object detection
         *  MobileNetv2-SSDLite
     *  Leaderboard
     *   *  Detection Results: VOC2012
     *  Tools
     *   *  BeaverDam: Video annotation tool for deep learning training labels
     *  Blogs
     *   *  Convolutional Neural Networks for Object Detection
         *  Introducing automatic object detection to visual search (Pinterest)
         *  Deep Learning for Object Detection with DIGITS
         *  Analyzing The Papers Behind Facebook’s Computer Vision Approach
         *  Easily Create High Quality Object Detectors with Deep Learning
         *  How to Train a Deep-Learned Object Detection Model in the Microsoft Cognitive Toolkit
         *  Object Detection in Satellite Imagery, a Low Overhead Approach
         *  You Only Look Twice — Multi-Scale Object Detection in Satellite Imagery With Convolutional Neural Networks
         *  Faster R-CNN Pedestrian and Car Detection
         *  Small U-Net for vehicle detection
         *  Region of interest pooling explained
         *  Supercharge your Computer Vision models with the TensorFlow Object Detection API
         *  Understanding SSD MultiBox — Real-Time Object Detection In Deep Learning
         *  One-shot object detection
         *  An overview of object detection: one-stage methods
     *  deep learning object detection

<table> 
 <thead> 
  <tr> 
   <th>Method</th> 
   <th align="center">backbone</th> 
   <th align="center">test size</th> 
   <th align="center">VOC2007</th> 
   <th align="center">VOC2010</th> 
   <th align="center">VOC2012</th> 
   <th align="center">ILSVRC 2013</th> 
   <th align="center">MSCOCO 2015</th> 
   <th align="center">Speed</th> 
  </tr> 
 </thead> 
 <tbody> 
  <tr> 
   <td>OverFeat</td> 
   <td align="center"></td> 
   <td align="center"></td> 
   <td align="center"></td> 
   <td align="center"></td> 
   <td align="center"></td> 
   <td align="center">24.3%</td> 
   <td align="center"></td> 
   <td align="center"></td> 
  </tr> 
  <tr> 
   <td>R-CNN</td> 
   <td align="center">AlexNet</td> 
   <td align="center"></td> 
   <td align="center">58.5%</td> 
   <td align="center">53.7%</td> 
   <td align="center">53.3%</td> 
   <td align="center">31.4%</td> 
   <td align="center"></td> 
   <td align="center"></td> 
  </tr> 
  <tr> 
   <td>R-CNN</td> 
   <td align="center">VGG17</td> 
   <td align="center"></td> 
   <td align="center">66.0%</td> 
   <td align="center"></td> 
   <td align="center"></td> 
   <td align="center"></td> 
   <td align="center"></td> 
   <td align="center"></td> 
  </tr> 
  <tr> 
   <td>SPP_net</td> 
   <td align="center">ZF-5</td> 
   <td align="center"></td> 
   <td align="center">54.2%</td> 
   <td align="center"></td> 
   <td align="center"></td> 
   <td align="center">31.84%</td> 
   <td align="center"></td> 
   <td align="center"></td> 
  </tr> 
  <tr> 
   <td>DeepID-Net</td> 
   <td align="center"></td> 
   <td align="center"></td> 
   <td align="center">64.1%</td> 
   <td align="center"></td> 
   <td align="center"></td> 
   <td align="center">50.3%</td> 
   <td align="center"></td> 
   <td align="center"></td> 
  </tr> 
  <tr> 
   <td>NoC</td> 
   <td align="center"></td> 
   <td align="center"></td> 
   <td align="center">73.3%</td> 
   <td align="center"></td> 
   <td align="center">68.8%</td> 
   <td align="center"></td> 
   <td align="center"></td> 
   <td align="center"></td> 
  </tr> 
  <tr> 
   <td>Fast-RCNN</td> 
   <td align="center">VGG16</td> 
   <td align="center"></td> 
   <td align="center">70.0%</td> 
   <td align="center">68.8%</td> 
   <td align="center">68.4%</td> 
   <td align="center"></td> 
   <td align="center">19.7%(@[0.5-0.95]), 35.9%(@0.5)</td> 
   <td align="center"></td> 
  </tr> 
  <tr> 
   <td>MR-CNN</td> 
   <td align="center"></td> 
   <td align="center"></td> 
   <td align="center">78.2%</td> 
   <td align="center"></td> 
   <td align="center">73.9%</td> 
   <td align="center"></td> 
   <td align="center"></td> 
   <td align="center"></td> 
  </tr> 
  <tr> 
   <td>Faster-RCNN</td> 
   <td align="center">VGG16</td> 
   <td align="center"></td> 
   <td align="center">78.8%</td> 
   <td align="center"></td> 
   <td align="center">75.9%</td> 
   <td align="center"></td> 
   <td align="center">21.9%(@[0.5-0.95]), 42.7%(@0.5)</td> 
   <td align="center">198ms</td> 
  </tr> 
  <tr> 
   <td>Faster-RCNN</td> 
   <td align="center">ResNet101</td> 
   <td align="center"></td> 
   <td align="center">85.6%</td> 
   <td align="center"></td> 
   <td align="center">83.8%</td> 
   <td align="center"></td> 
   <td align="center">37.4%(@[0.5-0.95]), 59.0%(@0.5)</td> 
   <td align="center"></td> 
  </tr> 
  <tr> 
   <td>YOLO</td> 
   <td align="center"></td> 
   <td align="center"></td> 
   <td align="center">63.4%</td> 
   <td align="center"></td> 
   <td align="center">57.9%</td> 
   <td align="center"></td> 
   <td align="center"></td> 
   <td align="center">45 fps</td> 
  </tr> 
  <tr> 
   <td>YOLO</td> 
   <td align="center">VGG-16</td> 
   <td align="center"></td> 
   <td align="center">66.4%</td> 
   <td align="center"></td> 
   <td align="center"></td> 
   <td align="center"></td> 
   <td align="center"></td> 
   <td align="center">21 fps</td> 
  </tr> 
  <tr> 
   <td>YOLOv2</td> 
   <td align="center"></td> 
   <td align="center">448x448</td> 
   <td align="center">78.6%</td> 
   <td align="center"></td> 
   <td align="center">73.4%</td> 
   <td align="center"></td> 
   <td align="center">21.6%(@[0.5-0.95]), 44.0%(@0.5)</td> 
   <td align="center">40 fps</td> 
  </tr> 
  <tr> 
   <td>SSD</td> 
   <td align="center">VGG16</td> 
   <td align="center">300x300</td> 
   <td align="center">77.2%</td> 
   <td align="center"></td> 
   <td align="center">75.8%</td> 
   <td align="center"></td> 
   <td align="center">25.1%(@[0.5-0.95]), 43.1%(@0.5)</td> 
   <td align="center">46 fps</td> 
  </tr> 
  <tr> 
   <td>SSD</td> 
   <td align="center">VGG16</td> 
   <td align="center">512x512</td> 
   <td align="center">79.8%</td> 
   <td align="center"></td> 
   <td align="center">78.5%</td> 
   <td align="center"></td> 
   <td align="center">28.8%(@[0.5-0.95]), 48.5%(@0.5)</td> 
   <td align="center">19 fps</td> 
  </tr> 
  <tr> 
   <td>SSD</td> 
   <td align="center">ResNet101</td> 
   <td align="center">300x300</td> 
   <td align="center"></td> 
   <td align="center"></td> 
   <td align="center"></td> 
   <td align="center"></td> 
   <td align="center">28.0%(@[0.5-0.95])</td> 
   <td align="center">16 fps</td> 
  </tr> 
  <tr> 
   <td>SSD</td> 
   <td align="center">ResNet101</td> 
   <td align="center">512x512</td> 
   <td align="center"></td> 
   <td align="center"></td> 
   <td align="center"></td> 
   <td align="center"></td> 
   <td align="center">31.2%(@[0.5-0.95])</td> 
   <td align="center">8 fps</td> 
  </tr> 
  <tr> 
   <td>DSSD</td> 
   <td align="center">ResNet101</td> 
   <td align="center">300x300</td> 
   <td align="center"></td> 
   <td align="center"></td> 
   <td align="center"></td> 
   <td align="center"></td> 
   <td align="center">28.0%(@[0.5-0.95])</td> 
   <td align="center">8 fps</td> 
  </tr> 
  <tr> 
   <td>DSSD</td> 
   <td align="center">ResNet101</td> 
   <td align="center">500x500</td> 
   <td align="center"></td> 
   <td align="center"></td> 
   <td align="center"></td> 
   <td align="center"></td> 
   <td align="center">33.2%(@[0.5-0.95])</td> 
   <td align="center">6 fps</td> 
  </tr> 
  <tr> 
   <td>ION</td> 
   <td align="center"></td> 
   <td align="center"></td> 
   <td align="center">79.2%</td> 
   <td align="center"></td> 
   <td align="center">76.4%</td> 
   <td align="center"></td> 
   <td align="center"></td> 
   <td align="center"></td> 
  </tr> 
  <tr> 
   <td>CRAFT</td> 
   <td align="center"></td> 
   <td align="center"></td> 
   <td align="center">75.7%</td> 
   <td align="center"></td> 
   <td align="center">71.3%</td> 
   <td align="center">48.5%</td> 
   <td align="center"></td> 
   <td align="center"></td> 
  </tr> 
  <tr> 
   <td>OHEM</td> 
   <td align="center"></td> 
   <td align="center"></td> 
   <td align="center">78.9%</td> 
   <td align="center"></td> 
   <td align="center">76.3%</td> 
   <td align="center"></td> 
   <td align="center">25.5%(@[0.5-0.95]), 45.9%(@0.5)</td> 
   <td align="center"></td> 
  </tr> 
  <tr> 
   <td>R-FCN</td> 
   <td align="center">ResNet50</td> 
   <td align="center"></td> 
   <td align="center">77.4%</td> 
   <td align="center"></td> 
   <td align="center"></td> 
   <td align="center"></td> 
   <td align="center"></td> 
   <td align="center">0.12sec(K40), 0.09sec(TitianX)</td> 
  </tr> 
  <tr> 
   <td>R-FCN</td> 
   <td align="center">ResNet101</td> 
   <td align="center"></td> 
   <td align="center">79.5%</td> 
   <td align="center"></td> 
   <td align="center"></td> 
   <td align="center"></td> 
   <td align="center"></td> 
   <td align="center">0.17sec(K40), 0.12sec(TitianX)</td> 
  </tr> 
  <tr> 
   <td>R-FCN(ms train)</td> 
   <td align="center">ResNet101</td> 
   <td align="center"></td> 
   <td align="center">83.6%</td> 
   <td align="center"></td> 
   <td align="center">82.0%</td> 
   <td align="center"></td> 
   <td align="center">31.5%(@[0.5-0.95]), 53.2%(@0.5)</td> 
   <td align="center"></td> 
  </tr> 
  <tr> 
   <td>PVANet 9.0</td> 
   <td align="center"></td> 
   <td align="center"></td> 
   <td align="center">84.9%</td> 
   <td align="center"></td> 
   <td align="center">84.2%</td> 
   <td align="center"></td> 
   <td align="center"></td> 
   <td align="center">750ms(CPU), 46ms(TitianX)</td> 
  </tr> 
  <tr> 
   <td>RetinaNet</td> 
   <td align="center">ResNet101-FPN</td> 
   <td align="center"></td> 
   <td align="center"></td> 
   <td align="center"></td> 
   <td align="center"></td> 
   <td align="center"></td> 
   <td align="center"></td> 
   <td align="center"></td> 
  </tr> 
  <tr> 
   <td>Light-Head R-CNN</td> 
   <td align="center">Xception*</td> 
   <td align="center">800/1200</td> 
   <td align="center"></td> 
   <td align="center"></td> 
   <td align="center"></td> 
   <td align="center"></td> 
   <td align="center">31.5%@[0.5:0.95]</td> 
   <td align="center">95 fps</td> 
  </tr> 
  <tr> 
   <td>Light-Head R-CNN</td> 
   <td align="center">Xception*</td> 
   <td align="center">700/1100</td> 
   <td align="center"></td> 
   <td align="center"></td> 
   <td align="center"></td> 
   <td align="center"></td> 
   <td align="center">30.7%@[0.5:0.95]</td> 
   <td align="center">102 fps</td> 
  </tr> 
  <tr> 
   <td>STDN</td> 
   <td align="center"></td> 
   <td align="center"></td> 
   <td align="center">80.9 (07+12)</td> 
   <td align="center"></td> 
   <td align="center"></td> 
   <td align="center"></td> 
   <td align="center"></td> 
   <td align="center"></td> 
  </tr> 
  <tr> 
   <td>RefineDet</td> 
   <td align="center"></td> 
   <td align="center"></td> 
   <td align="center">83.8 (07+12)</td> 
   <td align="center"></td> 
   <td align="center">83.5 (07++12)</td> 
   <td align="center"></td> 
   <td align="center">41.8</td> 
   <td align="center"></td> 
  </tr> 
  <tr> 
   <td>SNIP</td> 
   <td align="center"></td> 
   <td align="center"></td> 
   <td align="center"></td> 
   <td align="center"></td> 
   <td align="center"></td> 
   <td align="center"></td> 
   <td align="center">45.7</td> 
   <td align="center"></td> 
  </tr> 
  <tr> 
   <td>Relation-Network</td> 
   <td align="center"></td> 
   <td align="center"></td> 
   <td align="center"></td> 
   <td align="center"></td> 
   <td align="center"></td> 
   <td align="center"></td> 
   <td align="center">32.5</td> 
   <td align="center"></td> 
  </tr> 
  <tr> 
   <td>Cascade R-CNN</td> 
   <td align="center"></td> 
   <td align="center"></td> 
   <td align="center"></td> 
   <td align="center"></td> 
   <td align="center"></td> 
   <td align="center"></td> 
   <td align="center">42.8</td> 
   <td align="center"></td> 
  </tr> 
  <tr> 
   <td>MLKP</td> 
   <td align="center"></td> 
   <td align="center"></td> 
   <td align="center">80.6 (07+12)</td> 
   <td align="center"></td> 
   <td align="center">77.2 (07++12)</td> 
   <td align="center"></td> 
   <td align="center">28.6</td> 
   <td align="center"></td> 
  </tr> 
  <tr> 
   <td>Fitness-NMS</td> 
   <td align="center"></td> 
   <td align="center"></td> 
   <td align="center"></td> 
   <td align="center"></td> 
   <td align="center"></td> 
   <td align="center"></td> 
   <td align="center">41.8</td> 
   <td align="center"></td> 
  </tr> 
  <tr> 
   <td>RFBNet</td> 
   <td align="center"></td> 
   <td align="center"></td> 
   <td align="center">82.2 (07+12)</td> 
   <td align="center"></td> 
   <td align="center"></td> 
   <td align="center"></td> 
   <td align="center"></td> 
   <td align="center"></td> 
  </tr> 
  <tr> 
   <td>CornerNet</td> 
   <td align="center"></td> 
   <td align="center"></td> 
   <td align="center"></td> 
   <td align="center"></td> 
   <td align="center"></td> 
   <td align="center"></td> 
   <td align="center">42.1</td> 
   <td align="center"></td> 
  </tr> 
  <tr> 
   <td>PFPNet</td> 
   <td align="center"></td> 
   <td align="center"></td> 
   <td align="center">84.1 (07+12)</td> 
   <td align="center"></td> 
   <td align="center">83.7 (07++12)</td> 
   <td align="center"></td> 
   <td align="center">39.4</td> 
   <td align="center"></td> 
  </tr> 
  <tr> 
   <td>Pelee</td> 
   <td align="center"></td> 
   <td align="center"></td> 
   <td align="center">70.9 (07+12)</td> 
   <td align="center"></td> 
   <td align="center"></td> 
   <td align="center"></td> 
   <td align="center"></td> 
   <td align="center"></td> 
  </tr> 
  <tr> 
   <td>HKRM</td> 
   <td align="center"></td> 
   <td align="center"></td> 
   <td align="center">78.8 (07+12)</td> 
   <td align="center"></td> 
   <td align="center"></td> 
   <td align="center"></td> 
   <td align="center">37.8</td> 
   <td align="center"></td> 
  </tr> 
  <tr> 
   <td>M2Det</td> 
   <td align="center"></td> 
   <td align="center"></td> 
   <td align="center"></td> 
   <td align="center"></td> 
   <td align="center"></td> 
   <td align="center"></td> 
   <td align="center">44.2</td> 
   <td align="center"></td> 
  </tr> 
  <tr> 
   <td>SIN</td> 
   <td align="center"></td> 
   <td align="center"></td> 
   <td align="center">76.0 (07+12)</td> 
   <td align="center"></td> 
   <td align="center">73.1 (07++12)</td> 
   <td align="center"></td> 
   <td align="center">23.2</td> 
   <td align="center"></td> 
  </tr> 
 </tbody> 
</table>

# Papers #

--------------------

## 损失函数 ##

### \[CVPR2019\] Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression ###

*  arxiv: https://arxiv.org/abs/1902.09630

### Deep Neural Networks for Object Detection ###

*  paper: http://papers.nips.cc/paper/5207-deep-neural-networks-for-object-detection.pdf

### OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks ###

*  arxiv: http://arxiv.org/abs/1312.6229
 *  github: https://github.com/sermanet/OverFeat
 *  code: http://cilvr.nyu.edu/doku.php?id=software:overfeat:start

--------------------

## R-CNN ##

### Rich feature hierarchies for accurate object detection and semantic segmentation ###

*  intro: R-CNN
 *  arxiv: http://arxiv.org/abs/1311.2524
 *  supp: http://people.eecs.berkeley.edu/~rbg/papers/r-cnn-cvpr-supp.pdf
 *  slides: http://www.image-net.org/challenges/LSVRC/2013/slides/r-cnn-ilsvrc2013-workshop.pdf
 *  slides: http://www.cs.berkeley.edu/~rbg/slides/rcnn-cvpr14-slides.pdf
 *  github: https://github.com/rbgirshick/rcnn
 *  notes: http://zhangliliang.com/2014/07/23/paper-note-rcnn/
 *  caffe-pr(“Make R-CNN the Caffe detection example”): https://github.com/BVLC/caffe/pull/482

--------------------

## Fast R-CNN ##

### Fast R-CNN ###

*  arxiv: http://arxiv.org/abs/1504.08083
 *  slides: http://tutorial.caffe.berkeleyvision.org/caffe-cvpr15-detection.pdf
 *  github: https://github.com/rbgirshick/fast-rcnn
 *  github(COCO-branch): https://github.com/rbgirshick/fast-rcnn/tree/coco
 *  webcam demo: https://github.com/rbgirshick/fast-rcnn/pull/29
 *  notes: http://zhangliliang.com/2015/05/17/paper-note-fast-rcnn/
 *  notes: http://blog.csdn.net/linj\_m/article/details/48930179
 *  github(“Fast R-CNN in MXNet”): https://github.com/precedenceguo/mx-rcnn
 *  github: https://github.com/mahyarnajibi/fast-rcnn-torch
 *  github: https://github.com/apple2373/chainer-simple-fast-rnn
 *  github: https://github.com/zplizzi/tensorflow-fast-rcnn

### A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detection ###

*  intro: CVPR 2017
 *  arxiv: https://arxiv.org/abs/1704.03414
 *  paper: http://abhinavsh.info/papers/pdfs/adversarial\_object\_detection.pdf
 *  github(Caffe): https://github.com/xiaolonw/adversarial-frcnn

## Faster R-CNN ##

### Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks ###

*  intro: NIPS 2015
 *  arxiv: http://arxiv.org/abs/1506.01497
 *  gitxiv: http://www.gitxiv.com/posts/8pfpcvefDYn2gSgXk/faster-r-cnn-towards-real-time-object-detection-with-region
 *  slides: http://web.cs.hacettepe.edu.tr/~aykut/classes/spring2016/bil722/slides/w05-FasterR-CNN.pdf
 *  github(official, Matlab): https://github.com/ShaoqingRen/faster\_rcnn
 *  github: https://github.com/rbgirshick/py-faster-rcnn
 *  github(MXNet): https://github.com/msracver/Deformable-ConvNets/tree/master/faster\_rcnn
 *  github: https://github.com//jwyang/faster-rcnn.pytorch
 *  github: https://github.com/mitmul/chainer-faster-rcnn
 *  github: https://github.com/andreaskoepf/faster-rcnn.torch
 *  github: https://github.com/ruotianluo/Faster-RCNN-Densecap-torch
 *  github: https://github.com/smallcorgi/Faster-RCNN\_TF
 *  github: https://github.com/CharlesShang/TFFRCNN
 *  github(C++ demo): https://github.com/YihangLou/FasterRCNN-Encapsulation-Cplusplus
 *  github: https://github.com/yhenon/keras-frcnn
 *  github: https://github.com/Eniac-Xie/faster-rcnn-resnet
 *  github(C++): https://github.com/D-X-Y/caffe-faster-rcnn/tree/dev

### R-CNN minus R ###

*  intro: BMVC 2015
 *  arxiv: http://arxiv.org/abs/1506.06981

### Faster R-CNN in MXNet with distributed implementation and data parallelization ###

*  github: https://github.com/dmlc/mxnet/tree/master/example/rcnn

### Contextual Priming and Feedback for Faster R-CNN ###

*  intro: ECCV 2016. Carnegie Mellon University
 *  paper: http://abhinavsh.info/context\_priming\_feedback.pdf
 *  poster: http://www.eccv2016.org/files/posters/P-1A-20.pdf

### An Implementation of Faster RCNN with Study for Region Sampling ###

*  intro: Technical Report, 3 pages. CMU
 *  arxiv: https://arxiv.org/abs/1702.02138
 *  github: https://github.com/endernewton/tf-faster-rcnn

### Interpretable R-CNN ###

*  intro: North Carolina State University & Alibaba
 *  keywords: AND-OR Graph (AOG)
 *  arxiv: https://arxiv.org/abs/1711.05226

### \[AAAI2019\]Object Detection based on Region Decomposition and Assembly ###

*  intro: AAAI2019,区域分解组装
 *  arxiv: https://arxiv.org/abs/1901.08225
 *  translate: https://zhuanlan.zhihu.com/p/58951221 论文翻译

--------------------

## Light-Head R-CNN ##

### Light-Head R-CNN: In Defense of Two-Stage Object Detector ###

*  intro: Tsinghua University & Megvii Inc
 *  arxiv: https://arxiv.org/abs/1711.07264
 *  github(official, Tensorflow): https://github.com/zengarden/light\_head\_rcnn
 *  github: https://github.com/terrychenism/Deformable-ConvNets/blob/master/rfcn/symbols/resnet\_v1\_101\_rfcn\_light.py\#L784

\#\#Cascade R-CNN

### Cascade R-CNN: Delving into High Quality Object Detection ###

*  intro: CVPR 2018. UC San Diego
 *  arxiv: https://arxiv.org/abs/1712.00726
 *  github(Caffe, official): https://github.com/zhaoweicai/cascade-rcnn

--------------------

## MultiBox ##

### Scalable Object Detection using Deep Neural Networks ###

*  intro: first MultiBox. Train a CNN to predict Region of Interest.
 *  arxiv: http://arxiv.org/abs/1312.2249
 *  github: https://github.com/google/multibox
 *  blog: https://research.googleblog.com/2014/12/high-quality-object-detection-at-scale.html

### Scalable, High-Quality Object Detection ###

*  intro: second MultiBox
 *  arxiv: http://arxiv.org/abs/1412.1441
 *  github: https://github.com/google/multibox

--------------------

## SPP-Net ##

### Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition ###

*  intro: ECCV 2014 / TPAMI 2015
 *  arxiv: http://arxiv.org/abs/1406.4729
 *  github: https://github.com/ShaoqingRen/SPP\_net
 *  notes: http://zhangliliang.com/2014/09/13/paper-note-sppnet/

### DeepID-Net: Deformable Deep Convolutional Neural Networks for Object Detection ###

*  intro: PAMI 2016
 *  intro: an extension of R-CNN. box pre-training, cascade on region proposals, deformation layers and context representations
 *  project page: http://www.ee.cuhk.edu.hk/%CB%9Cwlouyang/projects/imagenetDeepId/index.html
 *  arxiv: http://arxiv.org/abs/1412.5661

### Object Detectors Emerge in Deep Scene CNNs ###

*  intro: ICLR 2015
 *  arxiv: http://arxiv.org/abs/1412.6856
 *  paper: https://www.robots.ox.ac.uk/~vgg/rg/papers/zhou\_iclr15.pdf
 *  paper: https://people.csail.mit.edu/khosla/papers/iclr2015\_zhou.pdf
 *  slides: http://places.csail.mit.edu/slide\_iclr2015.pdf

### segDeepM: Exploiting Segmentation and Context in Deep Neural Networks for Object Detection ###

*  intro: CVPR 2015
 *  project(code+data): https://www.cs.toronto.edu/~yukun/segdeepm.html
 *  arxiv: https://arxiv.org/abs/1502.04275
 *  github: https://github.com/YknZhu/segDeepM

### Object Detection Networks on Convolutional Feature Maps ###

*  intro: TPAMI 2015
 *  keywords: NoC
 *  arxiv: http://arxiv.org/abs/1504.06066

### Improving Object Detection with Deep Convolutional Networks via Bayesian Optimization and Structured Prediction ###

*  arxiv: http://arxiv.org/abs/1504.03293
 *  slides: http://www.ytzhang.net/files/publications/2015-cvpr-det-slides.pdf
 *  github: https://github.com/YutingZhang/fgs-obj

### DeepBox: Learning Objectness with Convolutional Networks ###

*  keywords: DeepBox
 *  arxiv: http://arxiv.org/abs/1505.02146
 *  github: https://github.com/weichengkuo/DeepBox

--------------------

## MR-CNN ##

### Object detection via a multi-region & semantic segmentation-aware CNN model ###

*  intro: ICCV 2015. MR-CNN
 *  arxiv: http://arxiv.org/abs/1505.01749
 *  github: https://github.com/gidariss/mrcnn-object-detection
 *  notes: http://zhangliliang.com/2015/05/17/paper-note-ms-cnn/
 *  notes: http://blog.cvmarcher.com/posts/2015/05/17/multi-region-semantic-segmentation-aware-cnn/

--------------------

## YOLO ##

### You Only Look Once: Unified, Real-Time Object Detection ###

![这里写图片描述][70]

*  arxiv: http://arxiv.org/abs/1506.02640
 *  code: http://pjreddie.com/darknet/yolo/
 *  github: https://github.com/pjreddie/darknet
 *  blog: https://pjreddie.com/publications/yolo/
 *  slides: https://docs.google.com/presentation/d/1aeRvtKG21KHdD5lg6Hgyhx5rPq\_ZOsGjG5rJ1HP7BbA/pub?start=false&loop=false&delayms=3000&slide=id.p
 *  reddit: https://www.reddit.com/r/MachineLearning/comments/3a3m0o/realtime\_object\_detection\_with\_yolo/
 *  github: https://github.com/gliese581gg/YOLO\_tensorflow
 *  github: https://github.com/xingwangsfu/caffe-yolo
 *  github: https://github.com/frankzhangrui/Darknet-Yolo
 *  github: https://github.com/BriSkyHekun/py-darknet-yolo
 *  github: https://github.com/tommy-qichang/yolo.torch
 *  github: https://github.com/frischzenger/yolo-windows
 *  github: https://github.com/AlexeyAB/yolo-windows
 *  github: https://github.com/nilboy/tensorflow-yolo

### darkflow - translate darknet to tensorflow. Load trained weights, retrain/fine-tune them using tensorflow, export constant graph def to C++ ###

*  blog: https://thtrieu.github.io/notes/yolo-tensorflow-graph-buffer-cpp
 *  github: https://github.com/thtrieu/darkflow

### Start Training YOLO with Our Own Data ###

![这里写图片描述][70 1]

*  intro: train with customized data and class numbers/labels. Linux / Windows version for darknet.
 *  blog: http://guanghan.info/blog/en/my-works/train-yolo/
 *  github: https://github.com/Guanghan/darknet

### YOLO: Core ML versus MPSNNGraph ###

*  intro: Tiny YOLO for iOS implemented using CoreML but also using the new MPS graph API.
 *  blog: http://machinethink.net/blog/yolo-coreml-versus-mps-graph/
 *  github: https://github.com/hollance/YOLO-CoreML-MPSNNGraph

### TensorFlow YOLO object detection on Android ###

*  intro: Real-time object detection on Android using the YOLO network with TensorFlow
 *  github: https://github.com/natanielruiz/android-yolo

### Computer Vision in iOS – Object Detection ###

*  blog: https://sriraghu.com/2017/07/12/computer-vision-in-ios-object-detection/
 *  github:https://github.com/r4ghu/iOS-CoreML-Yolo

--------------------

## YOLOv2 ##

### YOLO9000: Better, Faster, Stronger ###

*  arxiv: https://arxiv.org/abs/1612.08242
 *  code: http://pjreddie.com/yolo9000/
 *  github(Chainer): https://github.com/leetenki/YOLOv2
 *  github(Keras): https://github.com/allanzelener/YAD2K
 *  github(PyTorch): https://github.com/longcw/yolo2-pytorch
 *  github(Tensorflow): https://github.com/hizhangp/yolo\_tensorflow
 *  github(Windows): https://github.com/AlexeyAB/darknet
 *  github: https://github.com/choasUp/caffe-yolo9000
 *  github: https://github.com/philipperemy/yolo-9000

### darknet\_scripts ###

*  intro: Auxilary scripts to work with (YOLO) darknet deep learning famework. AKA -> How to generate YOLO anchors?
 *  github: https://github.com/Jumabek/darknet\_scripts

### Yolo\_mark: GUI for marking bounded boxes of objects in images for training Yolo v2 ###

*  github: https://github.com/AlexeyAB/Yolo\_mark

### LightNet: Bringing pjreddie’s DarkNet out of the shadows ###

*  github: https://github.com//explosion/lightnet

### YOLO v2 Bounding Box Tool ###

*  intro: Bounding box labeler tool to generate the training data in the format YOLO v2 requires.
 *  github: https://github.com/Cartucho/yolo-boundingbox-labeler-GUI

--------------------

## YOLOv3 ##

### YOLOv3: An Incremental Improvement ###

*  project page: https://pjreddie.com/darknet/yolo/
 *  arxiv: https://arxiv.org/abs/1804.02767
 *  github: https://github.com/DeNA/PyTorch\_YOLOv3
 *  github: https://github.com/eriklindernoren/PyTorch-YOLOv3

### YOLO-LITE: A Real-Time Object Detection Algorithm Optimized for Non-GPU Computers ###

*  arxiv：https://arxiv.org/abs/1811.05588

--------------------

### AttentionNet: Aggregating Weak Directions for Accurate Object Detection ###

*  intro: ICCV 2015
 *  intro: state-of-the-art performance of 65% (AP) on PASCAL VOC 2007/2012 human detection task
 *  arxiv: http://arxiv.org/abs/1506.07704
 *  slides: https://www.robots.ox.ac.uk/~vgg/rg/slides/AttentionNet.pdf
 *  slides: http://image-net.org/challenges/talks/lunit-kaist-slide.pdf

--------------------

## DenseBox ##

### DenseBox: Unifying Landmark Localization with End to End Object Detection ###

*  arxiv: http://arxiv.org/abs/1509.04874
 *  demo: http://pan.baidu.com/s/1mgoWWsS
 *  KITTI result: http://www.cvlibs.net/datasets/kitti/eval\_object.php

--------------------

## SSD ##

### SSD: Single Shot MultiBox Detector ###

![这里写图片描述][70 2]

*  intro: ECCV 2016 Oral
 *  arxiv: http://arxiv.org/abs/1512.02325
 *  paper: http://www.cs.unc.edu/~wliu/papers/ssd.pdf
 *  slides: http://www.cs.unc.edu/%7Ewliu/papers/ssd\_eccv2016\_slide.pdf
 *  github(Official): https://github.com/weiliu89/caffe/tree/ssd
 *  video: http://weibo.com/p/2304447a2326da963254c963c97fb05dd3a973
 *  github: https://github.com/zhreshold/mxnet-ssd
 *  github: https://github.com/zhreshold/mxnet-ssd.cpp
 *  github: https://github.com/rykov8/ssd\_keras
 *  github: https://github.com/balancap/SSD-Tensorflow
 *  github: https://github.com/amdegroot/ssd.pytorch
 *  github(Caffe): https://github.com/chuanqi305/MobileNet-SSD  
    What’s the diffience in performance between this new code you pushed and the previous code? \#327  
    https://github.com/weiliu89/caffe/issues/327

--------------------

## DSSD ##

### DSSD : Deconvolutional Single Shot Detector ###

*  intro: UNC Chapel Hill & Amazon Inc
 *  arxiv: https://arxiv.org/abs/1701.06659
 *  github: https://github.com/chengyangfu/caffe/tree/dssd
 *  github: https://github.com/MTCloudVision/mxnet-dssd
 *  demo: http://120.52.72.53/www.cs.unc.edu/c3pr90ntc0td/~cyfu/dssd\_lalaland.mp4

### Enhancement of SSD by concatenating feature maps for object detection ###

*  intro: rainbow SSD (R-SSD)
 *  arxiv: https://arxiv.org/abs/1705.09587

### Context-aware Single-Shot Detector ###

*  keywords: CSSD, DiCSSD, DeCSSD, effective receptive fields (ERFs), theoretical receptive fields (TRFs)
 *  arxiv: https://arxiv.org/abs/1707.08682

### Feature-Fused SSD: Fast Detection for Small Objects ###

https://arxiv.org/abs/1709.05054

--------------------

## FSSD ##

### FSSD: Feature Fusion Single Shot Multibox Detector ###

https://arxiv.org/abs/1712.00960

### Weaving Multi-scale Context for Single Shot Detector ###

*  intro: WeaveNet
 *  keywords: fuse multi-scale information
 *  arxiv: https://arxiv.org/abs/1712.03149

--------------------

## ESSD ##

### Extend the shallow part of Single Shot MultiBox Detector via Convolutional Neural Network ###

*  arxiv: https://arxiv.org/abs/1801.05918

### Tiny SSD: A Tiny Single-shot Detection Deep Convolutional Neural Network for Real-time Embedded Object Detection ###

*  arxiv: https://arxiv.org/abs/1802.06488

### MDSSD: Multi-scale Deconvolutional Single Shot Detector for small objects ###

*  intro: Zhengzhou University
 *  arxiv: https://arxiv.org/abs/1805.07009

--------------------

## Inside-Outside Net (ION) ##

### Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks ###

*  intro: “0.8s per image on a Titan X GPU (excluding proposal generation) without two-stage bounding-box regression and 1.15s per image with it”.
 *  arxiv: http://arxiv.org/abs/1512.04143
 *  slides: http://www.seanbell.ca/tmp/ion-coco-talk-bell2015.pdf
 *  coco-leaderboard: http://mscoco.org/dataset/\#detections-leaderboard

### Adaptive Object Detection Using Adjacency and Zoom Prediction ###

*  intro: CVPR 2016. AZ-Net
 *  arxiv: http://arxiv.org/abs/1512.07711
 *  github: https://github.com/luyongxi/az-net
 *  youtube: https://www.youtube.com/watch?v=YmFtuNwxaNM

### G-CNN: an Iterative Grid Based Object Detector ###

*  arxiv: http://arxiv.org/abs/1512.07729

--------------------

## Factors in Finetuning Deep Model for object detection ##

### Factors in Finetuning Deep Model for Object Detection with Long-tail Distribution ###

*  intro: CVPR 2016.rank 3rd for provided data and 2nd for external data on ILSVRC 2015 object detection
 *  project page: http://www.ee.cuhk.edu.hk/~wlouyang/projects/ImageNetFactors/CVPR16.html
 *  arxiv: http://arxiv.org/abs/1601.05150

### We don’t need no bounding-boxes: Training object class detectors using only human verification ###

*  arxiv: http://arxiv.org/abs/1602.08405

### HyperNet: Towards Accurate Region Proposal Generation and Joint Object Detection ###

*  arxiv: http://arxiv.org/abs/1604.00600

### A MultiPath Network for Object Detection ###

*  intro: BMVC 2016. Facebook AI Research (FAIR)
 *  arxiv: http://arxiv.org/abs/1604.02135
 *  github: https://github.com/facebookresearch/multipathnet

--------------------

## CRAFT ##

### CRAFT Objects from Images ###

*  intro: CVPR 2016. Cascade Region-proposal-network And FasT-rcnn. an extension of Faster R-CNN
 *  project page: http://byangderek.github.io/projects/craft.html
 *  arxiv: https://arxiv.org/abs/1604.03239
 *  paper: http://www.cv-foundation.org/openaccess/content\_cvpr\_2016/papers/Yang\_CRAFT\_Objects\_From\_CVPR\_2016\_paper.pdf
 *  github: https://github.com/byangderek/CRAFT

--------------------

## OHEM ##

### Training Region-based Object Detectors with Online Hard Example Mining ###

*  intro: CVPR 2016 Oral. Online hard example mining (OHEM)
 *  arxiv: http://arxiv.org/abs/1604.03540
 *  paper: http://www.cv-foundation.org/openaccess/content\_cvpr\_2016/papers/Shrivastava\_Training\_Region-Based\_Object\_CVPR\_2016\_paper.pdf
 *  github(Official): https://github.com/abhi2610/ohem
 *  author page: http://abhinav-shrivastava.info/

### S-OHEM: Stratified Online Hard Example Mining for Object Detection ###

*  arxiv: https://arxiv.org/abs/1705.02233

### Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers ###

*  intro: CVPR 2016
 *  keywords: scale-dependent pooling (SDP), cascaded rejection classifiers (CRC)
 *  paper: http://www-personal.umich.edu/~wgchoi/SDP-CRC\_camready.pdf

--------------------

## R-FCN ##

### R-FCN: Object Detection via Region-based Fully Convolutional Networks ###

arxiv: http://arxiv.org/abs/1605.06409  
github: https://github.com/daijifeng001/R-FCN  
github(MXNet): https://github.com/msracver/Deformable-ConvNets/tree/master/rfcn  
github: https://github.com/Orpine/py-R-FCN  
github: https://github.com/PureDiors/pytorch\_RFCN  
github: https://github.com/bharatsingh430/py-R-FCN-multiGPU  
github: https://github.com/xdever/RFCN-tensorflow

### R-FCN-3000 at 30fps: Decoupling Detection and Classification ###

*  arxiv: https://arxiv.org/abs/1712.01802

### Recycle deep features for better object detection ###

*  arxiv: http://arxiv.org/abs/1607.05066

--------------------

## MS-CNN ##

### A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection ###

*  intro: ECCV 2016
 *  intro: 640×480: 15 fps, 960×720: 8 fps
 *  arxiv: http://arxiv.org/abs/1607.07155
 *  github: https://github.com/zhaoweicai/mscnn
 *  poster: http://www.eccv2016.org/files/posters/P-2B-38.pdf

### Multi-stage Object Detection with Group Recursive Learning ###

*  intro: VOC2007: 78.6%, VOC2012: 74.9%
 *  arxiv: http://arxiv.org/abs/1608.05159

### Subcategory-aware Convolutional Neural Networks for Object Proposals and Detection ###

*  intro: WACV 2017. SubCNN
 *  arxiv: http://arxiv.org/abs/1604.04693
 *  github: https://github.com/tanshen/SubCNN

--------------------

## PVANET ##

### PVANet: Lightweight Deep Neural Networks for Real-time Object Detection ###

*  intro: Presented at NIPS 2016 Workshop on Efficient Methods for Deep Neural Networks (EMDNN). Continuation of arXiv:1608.08021
 *  arxiv: https://arxiv.org/abs/1611.08588
 *  github: https://github.com/sanghoon/pva-faster-rcnn
 *  leaderboard(PVANet 9.0): http://host.robots.ox.ac.uk:8080/leaderboard/displaylb.php?challengeid=11&compid=4

--------------------

## GBD-Net ##

### Gated Bi-directional CNN for Object Detection ###

*  intro: The Chinese University of Hong Kong & Sensetime Group Limited
 *  paper: http://link.springer.com/chapter/10.1007/978-3-319-46478-7\_22
 *  mirror: https://pan.baidu.com/s/1dFohO7v

### Crafting GBD-Net for Object Detection ###

*  intro: winner of the ImageNet object detection challenge of 2016. CUImage and CUVideo
 *  intro: gated bi-directional CNN (GBD-Net)
 *  arxiv: https://arxiv.org/abs/1610.02579
 *  github: https://github.com/craftGBD/craftGBD

### StuffNet: Using ‘Stuff’ to Improve Object Detection ###

*  arxiv: https://arxiv.org/abs/1610.05861

### Generalized Haar Filter based Deep Networks for Real-Time Object Detection in Traffic Scene ###

*  arxiv: https://arxiv.org/abs/1610.09609

### Hierarchical Object Detection with Deep Reinforcement Learning ###

*  intro: Deep Reinforcement Learning Workshop (NIPS 2016)
 *  project page: https://imatge-upc.github.io/detection-2016-nipsws/
 *  arxiv: https://arxiv.org/abs/1611.03718
 *  slides: http://www.slideshare.net/xavigiro/hierarchical-object-detection-with-deep-reinforcement-learning
 *  github: https://github.com/imatge-upc/detection-2016-nipsws
 *  blog: http://jorditorres.org/nips/

### Learning to detect and localize many objects from few examples ###

*  arxiv: https://arxiv.org/abs/1611.05664

### Speed/accuracy trade-offs for modern convolutional object detectors ###

*  intro: CVPR 2017. Google Research
 *  arxiv: https://arxiv.org/abs/1611.10012

### SqueezeDet: Unified, Small, Low Power Fully Convolutional Neural Networks for Real-Time Object Detection for Autonomous Driving ###

*  arxiv: https://arxiv.org/abs/1612.01051
 *  github: https://github.com/BichenWuUCB/squeezeDet
 *  github: https://github.com/fregu856/2D\_detection

--------------------

## Feature Pyramid Network (FPN) ##

### Feature Pyramid Networks for Object Detection ###

*  intro: Facebook AI Research
 *  arxiv: https://arxiv.org/abs/1612.03144

### Action-Driven Object Detection with Top-Down Visual Attentions ###

*  arxiv: https://arxiv.org/abs/1612.06704

### Beyond Skip Connections: Top-Down Modulation for Object Detection ###

*  intro: CMU & UC Berkeley & Google Research
 *  arxiv: https://arxiv.org/abs/1612.06851

### Wide-Residual-Inception Networks for Real-time Object Detection ###

*  intro: Inha University
 *  arxiv: https://arxiv.org/abs/1702.01243

### Attentional Network for Visual Object Detection ###

*  intro: University of Maryland & Mitsubishi Electric Research Laboratories
 *  arxiv: https://arxiv.org/abs/1702.01478

### Learning Chained Deep Features and Classifiers for Cascade in Object Detection ###

*  keykwords: CC-Net
 *  intro: chained cascade network (CC-Net). 81.1% mAP on PASCAL VOC 2007
 *  arxiv: https://arxiv.org/abs/1702.07054

### DeNet: Scalable Real-time Object Detection with Directed Sparse Sampling ###

*  intro: ICCV 2017 (poster)
 *  arxiv: https://arxiv.org/abs/1703.10295

### Discriminative Bimodal Networks for Visual Localization and Detection with Natural Language Queries ###

*  intro: CVPR 2017
 *  arxiv: https://arxiv.org/abs/1704.03944

### Spatial Memory for Context Reasoning in Object Detection ###

*  arxiv: https://arxiv.org/abs/1704.04224

### Accurate Single Stage Detector Using Recurrent Rolling Convolution ###

*  intro: CVPR 2017. SenseTime
 *  keywords: Recurrent Rolling Convolution (RRC)
 *  arxiv: https://arxiv.org/abs/1704.05776
 *  github: https://github.com/xiaohaoChen/rrc\_detection

### Deep Occlusion Reasoning for Multi-Camera Multi-Target Detection ###

*  arxiv: https://arxiv.org/abs/1704.05775

### LCDet: Low-Complexity Fully-Convolutional Neural Networks for Object Detection in Embedded Systems ###

*  intro: Embedded Vision Workshop in CVPR. UC San Diego & Qualcomm Inc
 *  arxiv: https://arxiv.org/abs/1705.05922

### Point Linking Network for Object Detection ###

*  intro: Point Linking Network (PLN)
 *  arxiv: https://arxiv.org/abs/1706.03646

### Perceptual Generative Adversarial Networks for Small Object Detection ###

*  arxiv: https://arxiv.org/abs/1706.05274

### Few-shot Object Detection ###

*  arxiv: https://arxiv.org/abs/1706.08249

### Yes-Net: An effective Detector Based on Global Information ###

*  arxiv: https://arxiv.org/abs/1706.09180

### SMC Faster R-CNN: Toward a scene-specialized multi-object detector ###

*  arxiv: https://arxiv.org/abs/1706.10217

### Towards lightweight convolutional neural networks for object detection ###

*  arxiv: https://arxiv.org/abs/1707.01395

### RON: Reverse Connection with Objectness Prior Networks for Object Detection ###

*  intro: CVPR 2017
 *  arxiv: https://arxiv.org/abs/1707.01691
 *  github: https://github.com/taokong/RON

### Mimicking Very Efficient Network for Object Detection ###

*  intro: CVPR 2017. SenseTime & Beihang University
 *  paper: http://openaccess.thecvf.com/content\_cvpr\_2017/papers/Li\_Mimicking\_Very\_Efficient\_CVPR\_2017\_paper.pdf

### Residual Features and Unified Prediction Network for Single Stage Detection ###

https://arxiv.org/abs/1707.05031

### Deformable Part-based Fully Convolutional Network for Object Detection ###

*  intro: BMVC 2017 (oral). Sorbonne Universités & CEDRIC
 *  arxiv: https://arxiv.org/abs/1707.06175

### Adaptive Feeding: Achieving Fast and Accurate Detections by Adaptively Combining Object Detectors ###

*  intro: ICCV 2017
 *  arxiv: https://arxiv.org/abs/1707.06399

### Recurrent Scale Approximation for Object Detection in CNN ###

*  intro: ICCV 2017
 *  keywords: Recurrent Scale Approximation (RSA)
 *  arxiv: https://arxiv.org/abs/1707.09531
 *  github: https://github.com/sciencefans/RSA-for-object-detection

--------------------

## DSOD ##

### DSOD: Learning Deeply Supervised Object Detectors from Scratch ###

![这里写图片描述][70 3]

*  intro: ICCV 2017. Fudan University & Tsinghua University & Intel Labs China
 *  arxiv: https://arxiv.org/abs/1708.01241
 *  github: https://github.com/szq0214/DSOD

### Object Detection from Scratch with Deep Supervision ###

*  arxiv: https://arxiv.org/abs/1809.09294

\#\#RetinaNet

### Focal Loss for Dense Object Detection ###

*  intro: ICCV 2017 Best student paper award. Facebook AI Research
 *  keywords: RetinaNet
 *  arxiv: https://arxiv.org/abs/1708.02002

### Focal Loss Dense Detector for Vehicle Surveillance ###

*  arxiv: https://arxiv.org/abs/1803.01114

### CoupleNet: Coupling Global Structure with Local Parts for Object Detection ###

*  intro: ICCV 2017
 *  arxiv: https://arxiv.org/abs/1708.02863

### Incremental Learning of Object Detectors without Catastrophic Forgetting ###

*  intro: ICCV 2017. Inria
 *  arxiv: https://arxiv.org/abs/1708.06977

### Zoom Out-and-In Network with Map Attention Decision for Region Proposal and Object Detection ###

*  arxiv: https://arxiv.org/abs/1709.04347

### StairNet: Top-Down Semantic Aggregation for Accurate One Shot Detection ###

*  arxiv: https://arxiv.org/abs/1709.05788

### Dynamic Zoom-in Network for Fast Object Detection in Large Images ###

https://arxiv.org/abs/1711.05187

### Zero-Annotation Object Detection with Web Knowledge Transfer ###

*  intro: NTU, Singapore & Amazon
 *  keywords: multi-instance multi-label domain adaption learning framework
 *  arxiv: https://arxiv.org/abs/1711.05954

--------------------

## MegDet ##

### MegDet: A Large Mini-Batch Object Detector ###

*  intro: Peking University & Tsinghua University & Megvii Inc
 *  arxiv: https://arxiv.org/abs/1711.07240

### Single-Shot Refinement Neural Network for Object Detection ###

*  arxiv: https://arxiv.org/abs/1711.06897
 *  github: https://github.com/sfzhang15/RefineDet
 *  github: https://github.com/MTCloudVision/RefineDet-Mxnet

### Receptive Field Block Net for Accurate and Fast Object Detection ###

*  intro: RFBNet
 *  arxiv: https://arxiv.org/abs/1711.07767
 *  github: https://github.com//ruinmessi/RFBNet

### An Analysis of Scale Invariance in Object Detection - SNIP ###

*  intro: CVPR 2018
 *  arxiv: https://arxiv.org/abs/1711.08189
 *  github: https://github.com/bharatsingh430/snip

### Feature Selective Networks for Object Detection ###

*  arxiv: https://arxiv.org/abs/1711.08879

### Learning a Rotation Invariant Detector with Rotatable Bounding Box ###

*  arxiv: https://arxiv.org/abs/1711.09405
 *  github(official, Caffe): https://github.com/liulei01/DRBox

### Scalable Object Detection for Stylized Objects ###

*  intro: Microsoft AI & Research Munich
 *  arxiv: https://arxiv.org/abs/1711.09822

### Learning Object Detectors from Scratch with Gated Recurrent Feature Pyramids ###

*  arxiv: https://arxiv.org/abs/1712.00886
 *  github: https://github.com/szq0214/GRP-DSOD

### Deep Regionlets for Object Detection ###

*  keywords: region selection network, gating network
 *  arxiv: https://arxiv.org/abs/1712.02408

### Training and Testing Object Detectors with Virtual Images ###

*  intro: IEEE/CAA Journal of Automatica Sinica
 *  arxiv: https://arxiv.org/abs/1712.08470

### Large-Scale Object Discovery and Detector Adaptation from Unlabeled Video ###

*  keywords: object mining, object tracking, unsupervised object discovery by appearance-based clustering, self-supervised detector adaptation
 *  arxiv: https://arxiv.org/abs/1712.08832

### Spot the Difference by Object Detection ###

*  intro: Tsinghua University & JD Group
 *  arxiv: https://arxiv.org/abs/1801.01051

### Localization-Aware Active Learning for Object Detection ###

*  arxiv: https://arxiv.org/abs/1801.05124

### Object Detection with Mask-based Feature Encoding ###

*  arxiv: https://arxiv.org/abs/1802.03934

### LSTD: A Low-Shot Transfer Detector for Object Detection ###

*  intro: AAAI 2018
 *  arxiv: https://arxiv.org/abs/1803.01529

### Domain Adaptive Faster R-CNN for Object Detection in the Wild ###

*  intro: CVPR 2018. ETH Zurich & ESAT/PSI
 *  arxiv: https://arxiv.org/abs/1803.03243
 *  github(official. Caffe): https://github.com/yuhuayc/da-faster-rcnn

### Pseudo Mask Augmented Object Detection ###

*  arxiv: https://arxiv.org/abs/1803.05858

### Revisiting RCNN: On Awakening the Classification Power of Faster RCNN ###

*  intro: ECCV 2018
 *  keywords: DCR V1
 *  arxiv: https://arxiv.org/abs/1803.06799
 *  github(official, MXNet): https://github.com/bowenc0221/Decoupled-Classification-Refinement

### Decoupled Classification Refinement: Hard False Positive Suppression for Object Detection ###

*  keywords: DCR V2
 *  arxiv: https://arxiv.org/abs/1810.04002
 *  github(official, MXNet): https://github.com/bowenc0221/Decoupled-Classification-Refinement

### Learning Region Features for Object Detection ###

*  intro: Peking University & MSRA
 *  arxiv: https://arxiv.org/abs/1803.07066

### Single-Shot Bidirectional Pyramid Networks for High-Quality Object Detection ###

*  intro: Singapore Management University & Zhejiang University
 *  arxiv: https://arxiv.org/abs/1803.08208

### Object Detection for Comics using Manga109 Annotations ###

*  intro: University of Tokyo & National Institute of Informatics, Japan
 *  arxiv: https://arxiv.org/abs/1803.08670

### Task-Driven Super Resolution: Object Detection in Low-resolution Images ###

*  arxiv: https://arxiv.org/abs/1803.11316

### Transferring Common-Sense Knowledge for Object Detection ###

*  arxiv: https://arxiv.org/abs/1804.01077

### Multi-scale Location-aware Kernel Representation for Object Detection ###

*  intro: CVPR 2018
 *  arxiv: https://arxiv.org/abs/1804.00428
 *  github: https://github.com/Hwang64/MLKP

### Loss Rank Mining: A General Hard Example Mining Method for Real-time Detectors ###

*  intro: National University of Defense Technology
 *  arxiv: https://arxiv.org/abs/1804.04606

### DetNet: A Backbone network for Object Detection ###

*  intro: Tsinghua University & Megvii Inc
 *  arxiv: https://arxiv.org/abs/1804.06215

### Robust Physical Adversarial Attack on Faster R-CNN Object Detector ###

*  arxiv: https://arxiv.org/abs/1804.05810

### AdvDetPatch: Attacking Object Detectors with Adversarial Patches ###

*  arxiv: https://arxiv.org/abs/1806.02299

### Attacking Object Detectors via Imperceptible Patches on Background ###

*  https://arxiv.org/abs/1809.05966

### Physical Adversarial Examples for Object Detectors ###

*  intro: WOOT 2018
 *  arxiv: https://arxiv.org/abs/1807.07769

### Quantization Mimic: Towards Very Tiny CNN for Object Detection ###

*  arxiv: https://arxiv.org/abs/1805.02152

### Object detection at 200 Frames Per Second ###

*  intro: United Technologies Research Center-Ireland
 *  arxiv: https://arxiv.org/abs/1805.06361

### Object Detection using Domain Randomization and Generative Adversarial Refinement of Synthetic Images ###

*  intro: CVPR 2018 Deep Vision Workshop
 *  arxiv: https://arxiv.org/abs/1805.11778

### SNIPER: Efficient Multi-Scale Training ###

*  intro: University of Maryland
 *  keywords: SNIPER (Scale Normalization for Image Pyramid with Efficient Resampling)
 *  arxiv: https://arxiv.org/abs/1805.09300
 *  github: https://github.com/mahyarnajibi/SNIPER

### Soft Sampling for Robust Object Detection ###

*  arxiv: https://arxiv.org/abs/1806.06986

### MetaAnchor: Learning to Detect Objects with Customized Anchors ###

*  intro: Megvii Inc (Face++) & Fudan University
 *  arxiv: https://arxiv.org/abs/1807.00980

### Localization Recall Precision (LRP): A New Performance Metric for Object Detection ###

*  intro: ECCV 2018. Middle East Technical University
 *  arxiv: https://arxiv.org/abs/1807.01696
 *  github: https://github.com/cancam/LRP

### Auto-Context R-CNN ###

*  intro: Rejected by ECCV18
 *  arxiv: https://arxiv.org/abs/1807.02842

### Pooling Pyramid Network for Object Detection ###

*  intro: Google AI Perception
 *  arxiv: https://arxiv.org/abs/1807.03284

### Modeling Visual Context is Key to Augmenting Object Detection Datasets ###

*  intro: ECCV 2018
 *  arxiv: https://arxiv.org/abs/1807.07428

### Dual Refinement Network for Single-Shot Object Detection ###

*  arxiv: https://arxiv.org/abs/1807.08638

### Acquisition of Localization Confidence for Accurate Object Detection ###

*  intro: ECCV 2018
 *  arxiv: https://arxiv.org/abs/1807.11590
 *  gihtub: https://github.com/vacancy/PreciseRoIPooling

### CornerNet: Detecting Objects as Paired Keypoints ###

*  intro: ECCV 2018
 *  keywords: IoU-Net, PreciseRoIPooling
 *  arxiv: https://arxiv.org/abs/1808.01244
 *  github: https://github.com/umich-vl/CornerNet

### Unsupervised Hard Example Mining from Videos for Improved Object Detection ###

*  intro: ECCV 2018
 *  arxiv: https://arxiv.org/abs/1808.04285

### SAN: Learning Relationship between Convolutional Features for Multi-Scale Object Detection ###

*  arxiv: https://arxiv.org/abs/1808.04974

### A Survey of Modern Object Detection Literature using Deep Learning ###

*  arxiv: https://arxiv.org/abs/1808.07256

### Tiny-DSOD: Lightweight Object Detection for Resource-Restricted Usages ###

*  intro: BMVC 2018
 *  arxiv: https://arxiv.org/abs/1807.11013
 *  github: https://github.com/lyxok1/Tiny-DSOD

### Deep Feature Pyramid Reconfiguration for Object Detection ###

*  intro: ECCV 2018
 *  arxiv: https://arxiv.org/abs/1808.07993

### MDCN: Multi-Scale, Deep Inception Convolutional Neural Networks for Efficient Object Detection ###

*  intro: ICPR 2018
 *  arxiv: https://arxiv.org/abs/1809.01791

### Recent Advances in Object Detection in the Age of Deep Convolutional Neural Networks ###

*  https://arxiv.org/abs/1809.03193

### Deep Learning for Generic Object Detection: A Survey ###

*  https://arxiv.org/abs/1809.02165

### Training Confidence-Calibrated Classifier for Detecting Out-of-Distribution Samples ###

*  intro: ICLR 2018
 *  arxiv: https://github.com/alinlab/Confident\_classifier

### ScratchDet:Exploring to Train Single-Shot Object Detectors from Scratch ###

*  arxiv: https://arxiv.org/abs/1810.08425
 *  github: https://github.com/KimSoybean/ScratchDet

### Fast and accurate object detection in high resolution 4K and 8K video using GPUs ###

*  intro: Best Paper Finalist at IEEE High Performance Extreme Computing Conference (HPEC) 2018
 *  intro: Carnegie Mellon University
 *  arxiv: https://arxiv.org/abs/1810.10551

### Hybrid Knowledge Routed Modules for Large-scale Object Detection ###

*  intro: NIPS 2018
 *  arxiv: https://arxiv.org/abs/1810.12681
 *  github(official, PyTorch): https://github.com/chanyn/HKRM

### Gradient Harmonized Single-stage Detector ###

*  intro: AAAI 2019
 *  arxiv: https://arxiv.org/abs/1811.05181

### M2Det: A Single-Shot Object Detector based on Multi-Level Feature Pyramid Network ###

*  intro: AAAI 2019
 *  arxiv: https://arxiv.org/abs/1811.04533
 *  github: https://github.com/qijiezhao/M2Det

### BAN: Focusing on Boundary Context for Object Detection ###

*  arxiv：https://arxiv.org/abs/1811.05243

### Multi-layer Pruning Framework for Compressing Single Shot MultiBox Detector ###

*  intro: WACV 2019
 *  arxiv: https://arxiv.org/abs/1811.08342

### R2CNN++: Multi-Dimensional Attention Based Rotation Invariant Detector with Robust Anchor Strategy ###

*  arxiv: https://arxiv.org/abs/1811.07126
 *  github: https://github.com/DetectionTeamUCAS/R2CNN-Plus-Plus\_Tensorflow

### DeRPN: Taking a further step toward more general object detection ###

*  intro: AAAI 2019
 *  intro: South China University of Technology
 *  arxiv: https://arxiv.org/abs/1811.06700
 *  github: https://github.com/HCIILAB/DeRPN

### Fast Efficient Object Detection Using Selective Attention ###

*  arxiv：https://arxiv.org/abs/1811.07502

### Sampling Techniques for Large-Scale Object Detection from Sparsely Annotated Objects ###

*  arxiv：https://arxiv.org/abs/1811.10862

### Efficient Coarse-to-Fine Non-Local Module for the Detection of Small Objects ###

*  arxiv：https://arxiv.org/abs/1811.12152

### Deep Regionlets: Blended Representation and Deep Learning for Generic Object Detection ###

*  arxiv：https://arxiv.org/abs/1811.11318

### Grid R-CNN ###

*  intro: SenseTime
 *  arxiv: https://arxiv.org/abs/1811.12030

### Transferable Adversarial Attacks for Image and Video Object Detection ###

\-arxiv：https://arxiv.org/abs/1811.12641

### Anchor Box Optimization for Object Detection ###

*  intro: University of Illinois at Urbana-Champaign & Microsoft Research
 *  arxiv: https://arxiv.org/abs/1812.00469

### AutoFocus: Efficient Multi-Scale Inference ###

*  intro: University of Maryland
 *  arxiv: https://arxiv.org/abs/1812.01600

\#\#\#Few-shot Object Detection via Feature Reweighting

*  arxiv：https://arxiv.org/abs/1812.01866

### Practical Adversarial Attack Against Object Detector ###

*  arxiv：https://arxiv.org/abs/1812.10217

### Learning Efficient Detector with Semi-supervised Adaptive Distillation ###

*  intro: SenseTime Research
 *  arxiv: https://arxiv.org/abs/1901.00366
 *  github: https://github.com/Tangshitao/Semi-supervised-Adaptive-Distillation

### Scale-Aware Trident Networks for Object Detection ###

intro: University of Chinese Academy of Sciences & TuSimple  
arxiv: https://arxiv.org/abs/1901.01892  
github: https://github.com/TuSimple/simpledet

### Region Proposal by Guided Anchoring ###

*  intro: CUHK - SenseTime Joint Lab & Amazon Rekognition & Nanyang Technological University
 *  arxiv: https://arxiv.org/abs/1901.03278

### Consistent Optimization for Single-Shot Object Detection ###

*  arxiv: https://arxiv.org/abs/1901.06563
 *  blog: https://zhuanlan.zhihu.com/p/55416312

### Bottom-up Object Detection by Grouping Extreme and Center Points ###

*  keywords: ExtremeNet
 *  arxiv: https://arxiv.org/abs/1901.08043
 *  github: https://github.com/xingyizhou/ExtremeNet

### A Single-shot Object Detector with Feature Aggragation and Enhancement ###

*  arxiv: https://arxiv.org/abs/1902.02923

### Bag of Freebies for Training Object Detection Neural Networks ###

*  intro: Amazon Web Services
 *  arxiv: https://arxiv.org/abs/1902.04103

--------------------

## Non-Maximum Suppression (NMS) ##

### End-to-End Integration of a Convolutional Network, Deformable Parts Model and Non-Maximum Suppression ###

*  intro: CVPR 2015
 *  arxiv: http://arxiv.org/abs/1411.5309
 *  paper: http://www.cv-foundation.org/openaccess/content\_cvpr\_2015/papers/Wan\_End-to-End\_Integration\_of\_2015\_CVPR\_paper.pdf

### A convnet for non-maximum suppression ###

*  arxiv: http://arxiv.org/abs/1511.06437  
    Improving Object Detection With One Line of Code

### Soft-NMS – Improving Object Detection With One Line of Code ###

*  intro: ICCV 2017. University of Maryland
 *  keywords: Soft-NMS
 *  arxiv: https://arxiv.org/abs/1704.04503
 *  github: https://github.com/bharatsingh430/soft-nms

### Learning non-maximum suppression ###

*  intro: CVPR 2017
 *  project page: https://www.mpi-inf.mpg.de/departments/computer-vision-and-multimodal-computing/research/object-recognition-and-scene-understanding/learning-nms/
 *  arxiv: https://arxiv.org/abs/1705.02950
 *  github: https://github.com/hosang/gossipnet

### Relation Networks for Object Detection ###

*  intro: CVPR 2018 oral
 *  arxiv: https://arxiv.org/abs/1711.11575
 *  github(official, MXNet): https://github.com/msracver/Relation-Networks-for-Object-Detection

### Learning Pairwise Relationship for Multi-object Detection in Crowded Scenes ###

*  keywords: Pairwise-NMS
 *  arxiv: https://arxiv.org/abs/1901.03796

### Daedalus: Breaking Non-Maximum Suppression in Object Detection via Adversarial Examples ###

*  arxiv: https://arxiv.org/abs/1902.02067

--------------------

## Adversarial Examples ##

### Adversarial Examples that Fool Detectors ###

*  intro: University of Illinois
 *  arxiv: https://arxiv.org/abs/1712.02494

### Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods ###

*  project page: http://nicholas.carlini.com/code/nn\_breaking\_detection/
 *  arxiv: https://arxiv.org/abs/1705.07263
 *  github: https://github.com/carlini/nn\_breaking\_detection

--------------------

## Weakly Supervised Object Detection ##

### Track and Transfer: Watching Videos to Simulate Strong Human Supervision for Weakly-Supervised Object Detection ###

*  intro: CVPR 2016
 *  arxiv: http://arxiv.org/abs/1604.05766

### Weakly supervised object detection using pseudo-strong labels ###

*  arxiv: http://arxiv.org/abs/1607.04731

### Saliency Guided End-to-End Learning for Weakly Supervised Object Detection ###

*  intro: IJCAI 2017
 *  arxiv: https://arxiv.org/abs/1706.06768

### Visual and Semantic Knowledge Transfer for Large Scale Semi-supervised Object Detection ###

*  intro: TPAMI 2017. National Institutes of Health (NIH) Clinical Center
 *  arxiv: https://arxiv.org/abs/1801.03145

--------------------

## Video Object Detection ##

### Learning Object Class Detectors from Weakly Annotated Video ###

*  intro: CVPR 2012
 *  paper: https://www.vision.ee.ethz.ch/publications/papers/proceedings/eth\_biwi\_00905.pdf

### Analysing domain shift factors between videos and images for object detection ###

*  arxiv: https://arxiv.org/abs/1501.01186

### Video Object Recognition ###

*  slides: http://vision.princeton.edu/courses/COS598/2015sp/slides/VideoRecog/Video%20Object%20Recognition.pptx

### Deep Learning for Saliency Prediction in Natural Video ###

*  intro: Submitted on 12 Jan 2016
 *  keywords: Deep learning, saliency map, optical flow, convolution network, contrast features
 *  paper: https://hal.archives-ouvertes.fr/hal-01251614/document

### T-CNN: Tubelets with Convolutional Neural Networks for Object Detection from Videos ###

*  intro: Winning solution in ILSVRC2015 Object Detection from Video(VID) Task
 *  arxiv: http://arxiv.org/abs/1604.02532
 *  github: https://github.com/myfavouritekk/T-CNN

### Object Detection from Video Tubelets with Convolutional Neural Networks ###

*  intro: CVPR 2016 Spotlight paper
 *  arxiv: https://arxiv.org/abs/1604.04053
 *  paper: http://www.ee.cuhk.edu.hk/~wlouyang/Papers/KangVideoDet\_CVPR16.pdf
 *  gihtub: https://github.com/myfavouritekk/vdetlib

### Object Detection in Videos with Tubelets and Multi-context Cues ###

*  intro: SenseTime Group
 *  slides: http://www.ee.cuhk.edu.hk/~xgwang/CUvideo.pdf
 *  slides: http://image-net.org/challenges/talks/Object%20Detection%20in%20Videos%20with%20Tubelets%20and%20Multi-context%20Cues%20-%20Final.pdf

### Context Matters: Refining Object Detection in Video with Recurrent Neural Networks ###

*  intro: BMVC 2016
 *  keywords: pseudo-labeler
 *  arxiv: http://arxiv.org/abs/1607.04648
 *  paper: http://vision.cornell.edu/se3/wp-content/uploads/2016/07/video\_object\_detection\_BMVC.pdf

### CNN Based Object Detection in Large Video Images ###

*  intro: WangTao @ 爱奇艺
 *  keywords: object retrieval, object detection, scene classification
 *  slides: http://on-demand.gputechconf.com/gtc/2016/presentation/s6362-wang-tao-cnn-based-object-detection-large-video-images.pdf

### Object Detection in Videos with Tubelet Proposal Networks ###

*  arxiv: https://arxiv.org/abs/1702.06355

### Flow-Guided Feature Aggregation for Video Object Detection ###

*  intro: MSRA
 *  arxiv: https://arxiv.org/abs/1703.10025

### Video Object Detection using Faster R-CNN ###

*  blog: http://andrewliao11.github.io/object\_detection/faster\_rcnn/
 *  github: https://github.com/andrewliao11/py-faster-rcnn-imagenet

### Improving Context Modeling for Video Object Detection and Tracking ###

http://image-net.org/challenges/talks\_2017/ilsvrc2017\_short(poster).pdf

### Temporal Dynamic Graph LSTM for Action-driven Video Object Detection ###

*  intro: ICCV 2017
 *  arxiv: https://arxiv.org/abs/1708.00666

### Mobile Video Object Detection with Temporally-Aware Feature Maps ###

*  arxiv: https://arxiv.org/abs/1711.06368

### Towards High Performance Video Object Detection ###

*  arxiv: https://arxiv.org/abs/1711.11577

### Impression Network for Video Object Detection ###

*  arxiv: https://arxiv.org/abs/1712.05896

### Spatial-Temporal Memory Networks for Video Object Detection ###

*  arxiv: https://arxiv.org/abs/1712.06317

### 3D-DETNet: a Single Stage Video-Based Vehicle Detector ###

*  arxiv: https://arxiv.org/abs/1801.01769

### Object Detection in Videos by Short and Long Range Object Linking ###

*  arxiv: https://arxiv.org/abs/1801.09823

### Object Detection in Video with Spatiotemporal Sampling Networks ###

*  intro: University of Pennsylvania, 2Dartmouth College
 *  arxiv: https://arxiv.org/abs/1803.05549

### Towards High Performance Video Object Detection for Mobiles ###

*  intro: Microsoft Research Asia
 *  arxiv: https://arxiv.org/abs/1804.05830

### Optimizing Video Object Detection via a Scale-Time Lattice ###

*  intro: CVPR 2018
 *  project page: http://mmlab.ie.cuhk.edu.hk/projects/ST-Lattice/
 *  arxiv: https://arxiv.org/abs/1804.05472
 *  github: https://github.com/hellock/scale-time-lattice

### Pack and Detect: Fast Object Detection in Videos Using Region-of-Interest Packing ###

*  https://arxiv.org/abs/1809.01701

### Fast Object Detection in Compressed Video ###

*  arxiv：https://arxiv.org/abs/1811.11057

### Tube-CNN: Modeling temporal evolution of appearance for object detection in video ###

*  intro: INRIA/ENS
 *  arxiv: https://arxiv.org/abs/1812.02619

### AdaScale: Towards Real-time Video Object Detection Using Adaptive Scaling ###

*  intro: SysML 2019 oral
 *  arxiv: https://arxiv.org/abs/1902.02910

--------------------

## Object Detection on Mobile Devices ##

### Pelee: A Real-Time Object Detection System on Mobile Devices ###

*  intro: ICLR 2018 workshop track
 *  intro: based on the SSD
 *  arxiv: https://arxiv.org/abs/1804.06882
 *  github: https://github.com/Robert-JunWang/Pelee

--------------------

## Object Detection in 3D ##

### Vote3Deep: Fast Object Detection in 3D Point Clouds Using Efficient Convolutional Neural Networks ###

*  arxiv: https://arxiv.org/abs/1609.06666

### Complex-YOLO: Real-time 3D Object Detection on Point Clouds ###

*  intro: Valeo Schalter und Sensoren GmbH & Ilmenau University of Technology
 *  arxiv: https://arxiv.org/abs/1803.06199

### Focal Loss in 3D Object Detection ###

*  arxiv: https://arxiv.org/abs/1809.06065
 *  github: https://github.com/pyun-ram/FL3D

### 3D Object Detection Using Scale Invariant and Feature Reweighting Networks ###

*  intro: AAAI 2019
 *  arxiv: https://arxiv.org/abs/1901.02237

### 3D Backbone Network for 3D Object Detection ###

*  arxiv: https://arxiv.org/abs/1901.08373

--------------------

## Object Detection on RGB-D ##

### Learning Rich Features from RGB-D Images for Object Detection and Segmentation ###

*  arxiv: http://arxiv.org/abs/1407.5736

### Differential Geometry Boosts Convolutional Neural Networks for Object Detection ###

*  intro: CVPR 2016
 *  paper: http://www.cv-foundation.org/openaccess/content\_cvpr\_2016\_workshops/w23/html/Wang\_Differential\_Geometry\_Boosts\_CVPR\_2016\_paper.html

### A Self-supervised Learning System for Object Detection using Physics Simulation and Multi-view Pose Estimation ###

*  arxiv: https://arxiv.org/abs/1703.03347

--------------------

## Zero-Shot Object Detection ##

### Zero-Shot Detection ###

*  intro: Australian National University
 *  keywords: YOLO
 *  arxiv: https://arxiv.org/abs/1803.07113

### Zero-Shot Object Detection ###

*  arxiv: https://arxiv.org/abs/1804.04340

### Zero-Shot Object Detection: Learning to Simultaneously Recognize and Localize Novel Concepts ###

*  intro: Australian National University
 *  arxiv: https://arxiv.org/abs/1803.06049

### Zero-Shot Object Detection by Hybrid Region Embedding ###

*  intro: Middle East Technical University & Hacettepe University
 *  arxiv: https://arxiv.org/abs/1805.06157

--------------------

## Salient Object Detection ##

This task involves predicting the salient regions of an image given by human eye fixations.

### Best Deep Saliency Detection Models (CVPR 2016 & 2015) ###

*  page: http://i.cs.hku.hk/~yzyu/vision.html

### Large-scale optimization of hierarchical features for saliency prediction in natural images ###

*  paper: http://coxlab.org/pdfs/cvpr2014\_vig\_saliency.pdf

### Predicting Eye Fixations using Convolutional Neural Networks ###

*  paper: http://www.escience.cn/system/file?fileId=72648

### Saliency Detection by Multi-Context Deep Learning ###

*  paper: http://www.cv-foundation.org/openaccess/content\_cvpr\_2015/papers/Zhao\_Saliency\_Detection\_by\_2015\_CVPR\_paper.pdf

### DeepSaliency: Multi-Task Deep Neural Network Model for Salient Object Detection ###

*  arxiv: http://arxiv.org/abs/1510.05484

### SuperCNN: A Superpixelwise Convolutional Neural Network for Salient Object Detection ###

*  paper: www.shengfenghe.com/supercnn-a-superpixelwise-convolutional-neural-network-for-salient-object-detection.html

### Shallow and Deep Convolutional Networks for Saliency Prediction ###

*  intro: CVPR 2016
 *  arxiv: http://arxiv.org/abs/1603.00845
 *  github: https://github.com/imatge-upc/saliency-2016-cvpr

### Recurrent Attentional Networks for Saliency Detection ###

*  intro: CVPR 2016. recurrent attentional convolutional-deconvolution network (RACDNN)
 *  arxiv: http://arxiv.org/abs/1604.03227

### Two-Stream Convolutional Networks for Dynamic Saliency Prediction ###

*  arxiv: http://arxiv.org/abs/1607.04730

--------------------

## Unconstrained Salient Object Detection ##

### Unconstrained Salient Object Detection via Proposal Subset Optimization ###

![这里写图片描述][70 4]

*  intro: CVPR 2016
 *  project page: http://cs-people.bu.edu/jmzhang/sod.html
 *  paper: http://cs-people.bu.edu/jmzhang/SOD/CVPR16SOD\_camera\_ready.pdf
 *  github: https://github.com/jimmie33/SOD
 *  caffe model zoo: https://github.com/BVLC/caffe/wiki/Model-Zoo\#cnn-object-proposal-models-for-salient-object-detection

### DHSNet: Deep Hierarchical Saliency Network for Salient Object Detection ###

*  paper: http://www.cv-foundation.org/openaccess/content\_cvpr\_2016/papers/Liu\_DHSNet\_Deep\_Hierarchical\_CVPR\_2016\_paper.pdf

### Salient Object Subitizing ###

![这里写图片描述][70 5]

*  intro: CVPR 2015
 *  intro: predicting the existence and the number of salient objects in an image using holistic cues
 *  project page: http://cs-people.bu.edu/jmzhang/sos.html
 *  arxiv: http://arxiv.org/abs/1607.07525
 *  paper: http://cs-people.bu.edu/jmzhang/SOS/SOS\_preprint.pdf
 *  caffe model zoo: https://github.com/BVLC/caffe/wiki/Model-Zoo\#cnn-models-for-salient-object-subitizing

### Deeply-Supervised Recurrent Convolutional Neural Network for Saliency Detection ###

*  intro: ACMMM 2016. deeply-supervised recurrent convolutional neural network (DSRCNN)
 *  arxiv: http://arxiv.org/abs/1608.05177

### Saliency Detection via Combining Region-Level and Pixel-Level Predictions with CNNs ###

*  intro: ECCV 2016
 *  arxiv: http://arxiv.org/abs/1608.05186

### Edge Preserving and Multi-Scale Contextual Neural Network for Salient Object Detection ###

*  arxiv: http://arxiv.org/abs/1608.08029

### A Deep Multi-Level Network for Saliency Prediction ###

*  arxiv: http://arxiv.org/abs/1609.01064

### Visual Saliency Detection Based on Multiscale Deep CNN Features ###

*  intro: IEEE Transactions on Image Processing
 *  arxiv: http://arxiv.org/abs/1609.02077

### A Deep Spatial Contextual Long-term Recurrent Convolutional Network for Saliency Detection ###

*  intro: DSCLRCN
 *  arxiv: https://arxiv.org/abs/1610.01708

### Deeply supervised salient object detection with short connections ###

*  intro: IEEE TPAMI 2018 (IEEE CVPR 2017)
 *  arxiv: https://arxiv.org/abs/1611.04849
 *  github(official, Caffe): https://github.com/Andrew-Qibin/DSS
 *  github(Tensorflow): https://github.com/Joker316701882/Salient-Object-Detection

### Weakly Supervised Top-down Salient Object Detection ###

*  intro: Nanyang Technological University
 *  arxiv: https://arxiv.org/abs/1611.05345

### SalGAN: Visual Saliency Prediction with Generative Adversarial Networks ###

*  project page: https://imatge-upc.github.io/saliency-salgan-2017/
 *  arxiv: https://arxiv.org/abs/1701.01081

### Visual Saliency Prediction Using a Mixture of Deep Neural Networks ###

*  arxiv: https://arxiv.org/abs/1702.00372

### A Fast and Compact Salient Score Regression Network Based on Fully Convolutional Network ###

*  arxiv: https://arxiv.org/abs/1702.00615

### Saliency Detection by Forward and Backward Cues in Deep-CNNs ###

*  arxiv: https://arxiv.org/abs/1703.00152

### Supervised Adversarial Networks for Image Saliency Detection ###

*  arxiv: https://arxiv.org/abs/1704.07242

### Group-wise Deep Co-saliency Detection ###

*  arxiv: https://arxiv.org/abs/1707.07381

### Towards the Success Rate of One: Real-time Unconstrained Salient Object Detection ###

*  intro: University of Maryland College Park & eBay Inc
 *  arxiv: https://arxiv.org/abs/1708.00079

### Amulet: Aggregating Multi-level Convolutional Features for Salient Object Detection ###

*  intro: ICCV 2017
 *  arixv: https://arxiv.org/abs/1708.02001

### Learning Uncertain Convolutional Features for Accurate Saliency Detection ###

*  intro: Accepted as a poster in ICCV 2017
 *  arxiv: https://arxiv.org/abs/1708.02031

### Deep Edge-Aware Saliency Detection ###

*  arxiv: https://arxiv.org/abs/1708.04366

### Self-explanatory Deep Salient Object Detection ###

*  intro: National University of Defense Technology, China & National University of Singapore
 *  arxiv: https://arxiv.org/abs/1708.05595

### PiCANet: Learning Pixel-wise Contextual Attention in ConvNets and Its Application in Saliency Detection ###

*  arxiv: https://arxiv.org/abs/1708.06433

### DeepFeat: A Bottom Up and Top Down Saliency Model Based on Deep Features of Convolutional Neural Nets ###

*  arxiv: https://arxiv.org/abs/1709.02495

### Recurrently Aggregating Deep Features for Salient Object Detection ###

*  intro: AAAI 2018
 *  paper: https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16775/16281

### Deep saliency: What is learnt by a deep network about saliency? ###

*  intro: 2nd Workshop on Visualisation for Deep Learning in the 34th International Conference On Machine Learning
 *  arxiv: https://arxiv.org/abs/1801.04261

### Contrast-Oriented Deep Neural Networks for Salient Object Detection ###

*  intro: TNNLS
 *  arxiv: https://arxiv.org/abs/1803.11395

### Salient Object Detection by Lossless Feature Reflection ###

*  intro: IJCAI 2018
 *  arxiv: https://arxiv.org/abs/1802.06527

### HyperFusion-Net: Densely Reflective Fusion for Salient Object Detection ###

*  arxiv: https://arxiv.org/abs/1804.05142

--------------------

## Video Saliency Detection ##

### Deep Learning For Video Saliency Detection ###

*  arxiv: https://arxiv.org/abs/1702.00871

### Video Salient Object Detection Using Spatiotemporal Deep Features ###

*  arxiv: https://arxiv.org/abs/1708.01447

### Predicting Video Saliency with Object-to-Motion CNN and Two-layer Convolutional LSTM ###

*  arxiv: https://arxiv.org/abs/1709.06316

--------------------

## Visual Relationship Detection ##

### Visual Relationship Detection with Language Priors ###

*  intro: ECCV 2016 oral
 *  paper: https://cs.stanford.edu/people/ranjaykrishna/vrd/vrd.pdf
 *  github: https://github.com/Prof-Lu-Cewu/Visual-Relationship-Detection

### ViP-CNN: A Visual Phrase Reasoning Convolutional Neural Network for Visual Relationship Detection ###

*  intro: Visual Phrase reasoning Convolutional Neural Network (ViP-CNN), Visual Phrase Reasoning Structure (VPRS)
 *  arxiv: https://arxiv.org/abs/1702.07191

### Visual Translation Embedding Network for Visual Relation Detection ###

*  arxiv: https://www.arxiv.org/abs/1702.08319

### Deep Variation-structured Reinforcement Learning for Visual Relationship and Attribute Detection ###

*  intro: CVPR 2017 spotlight paper
 *  arxiv: https://arxiv.org/abs/1703.03054

### Detecting Visual Relationships with Deep Relational Networks ###

*  intro: CVPR 2017 oral. The Chinese University of Hong Kong
 *  arxiv: https://arxiv.org/abs/1704.03114

### Identifying Spatial Relations in Images using Convolutional Neural Networks ###

*  arxiv: https://arxiv.org/abs/1706.04215

### PPR-FCN: Weakly Supervised Visual Relation Detection via Parallel Pairwise R-FCN ###

*  intro: ICCV
 *  arxiv: https://arxiv.org/abs/1708.01956

### Natural Language Guided Visual Relationship Detection ###

*  arxiv: https://arxiv.org/abs/1711.06032

### Detecting Visual Relationships Using Box Attention ###

*  intro: Google AI & IST Austria
 *  arxiv: https://arxiv.org/abs/1807.02136

### Google AI Open Images - Visual Relationship Track ###

*  intro: Detect pairs of objects in particular relationships
 *  kaggle: https://www.kaggle.com/c/google-ai-open-images-visual-relationship-track

### Context-Dependent Diffusion Network for Visual Relationship Detection ###

*  intro: 2018 ACM Multimedia Conference
 *  arxiv: https://arxiv.org/abs/1809.06213

### A Problem Reduction Approach for Visual Relationships Detection ###

*  intro: ECCV 2018 Workshop
 *  arxiv: https://arxiv.org/abs/1809.09828

--------------------

## Face Deteciton ##

### Multi-view Face Detection Using Deep Convolutional Neural Networks ###

*  intro: Yahoo
 *  arxiv: http://arxiv.org/abs/1502.02766
 *  github: https://github.com/guoyilin/FaceDetection\_CNN

### From Facial Parts Responses to Face Detection: A Deep Learning Approach ###

*  intro: ICCV 2015. CUHK
 *  project page: http://personal.ie.cuhk.edu.hk/~ys014/projects/Faceness/Faceness.html
 *  arxiv: https://arxiv.org/abs/1509.06451
 *  paper: http://www.cv-foundation.org/openaccess/content\_iccv\_2015/papers/Yang\_From\_Facial\_Parts\_ICCV\_2015\_paper.pdf

### Compact Convolutional Neural Network Cascade for Face Detection ###

*  arxiv: http://arxiv.org/abs/1508.01292
 *  github: https://github.com/Bkmz21/FD-Evaluation
 *  github: https://github.com/Bkmz21/CompactCNNCascade

### Face Detection with End-to-End Integration of a ConvNet and a 3D Model ###

*  intro: ECCV 2016
 *  arxiv: https://arxiv.org/abs/1606.00850
 *  github(MXNet): https://github.com/tfwu/FaceDetection-ConvNet-3D

### CMS-RCNN: Contextual Multi-Scale Region-based CNN for Unconstrained Face Detection ###

*  intro: CMU
 *  arxiv: https://arxiv.org/abs/1606.05413

### Towards a Deep Learning Framework for Unconstrained Face Detection ###

*  intro: overlap with CMS-RCNN
 *  arxiv: https://arxiv.org/abs/1612.05322

### Supervised Transformer Network for Efficient Face Detection ###

*  arxiv: http://arxiv.org/abs/1607.05477

### UnitBox: An Advanced Object Detection Network ###

*  intro: ACM MM 2016
 *  keywords: IOULoss
 *  arxiv: http://arxiv.org/abs/1608.01471

### Bootstrapping Face Detection with Hard Negative Examples ###

*  author: 万韶华 @ 小米.
 *  intro: Faster R-CNN, hard negative mining. state-of-the-art on the FDDB dataset
 *  arxiv: http://arxiv.org/abs/1608.02236

### Grid Loss: Detecting Occluded Faces ###

*  intro: ECCV 2016
 *  arxiv: https://arxiv.org/abs/1609.00129
 *  paper: http://lrs.icg.tugraz.at/pubs/opitz\_eccv\_16.pdf
 *  poster: http://www.eccv2016.org/files/posters/P-2A-34.pdf

### A Multi-Scale Cascade Fully Convolutional Network Face Detector ###

*  intro: ICPR 2016
 *  arxiv: http://arxiv.org/abs/1609.03536

--------------------

## MTCNN ##

### Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Neural Networks ###

![这里写图片描述][70 6]

*  project page: https://kpzhang93.github.io/MTCNN\_face\_detection\_alignment/index.html
 *  arxiv: https://arxiv.org/abs/1604.02878
 *  github(official, Matlab): https://github.com/kpzhang93/MTCNN\_face\_detection\_alignment
 *  github: https://github.com/pangyupo/mxnet\_mtcnn\_face\_detection
 *  github: https://github.com/DaFuCoding/MTCNN\_Caffe
 *  github(MXNet): https://github.com/Seanlinx/mtcnn
 *  github: https://github.com/Pi-DeepLearning/RaspberryPi-FaceDetection-MTCNN-Caffe-With-Motion
 *  github(Caffe): https://github.com/foreverYoungGitHub/MTCNN
 *  github: https://github.com/CongWeilin/mtcnn-caffe
 *  github(OpenCV+OpenBlas): https://github.com/AlphaQi/MTCNN-light
 *  github(Tensorflow+golang): https://github.com/jdeng/goface

### Face Detection using Deep Learning: An Improved Faster RCNN Approach ###

*  intro: DeepIR Inc
 *  arxiv: https://arxiv.org/abs/1701.08289

### Faceness-Net: Face Detection through Deep Facial Part Responses ###

*  intro: An extended version of ICCV 2015 paper
 *  arxiv: https://arxiv.org/abs/1701.08393

### Multi-Path Region-Based Convolutional Neural Network for Accurate Detection of Unconstrained “Hard Faces” ###

*  intro: CVPR 2017. MP-RCNN, MP-RPN
 *  arxiv: https://arxiv.org/abs/1703.09145

### End-To-End Face Detection and Recognition ###

*  arxiv: https://arxiv.org/abs/1703.10818

### Face R-CNN ###

*  arxiv: https://arxiv.org/abs/1706.01061

### Face Detection through Scale-Friendly Deep Convolutional Networks ###

*  arxiv: https://arxiv.org/abs/1706.02863

### Scale-Aware Face Detection ###

*  intro: CVPR 2017. SenseTime & Tsinghua University
 *  arxiv: https://arxiv.org/abs/1706.09876

### Detecting Faces Using Inside Cascaded Contextual CNN ###

*  intro: CVPR 2017. Tencent AI Lab & SenseTime
 *  paper: http://ai.tencent.com/ailab/media/publications/Detecting\_Faces\_Using\_Inside\_Cascaded\_Contextual\_CNN.pdf

### Multi-Branch Fully Convolutional Network for Face Detection ###

*  arxiv: https://arxiv.org/abs/1707.06330

### SSH: Single Stage Headless Face Detector ###

*  intro: ICCV 2017. University of Maryland
 *  arxiv: https://arxiv.org/abs/1708.03979
 *  github(official, Caffe): https://github.com/mahyarnajibi/SSH

### Dockerface: an easy to install and use Faster R-CNN face detector in a Docker container ###

*  arxiv: https://arxiv.org/abs/1708.04370

### FaceBoxes: A CPU Real-time Face Detector with High Accuracy ###

*  intro: IJCB 2017
 *  keywords: Rapidly Digested Convolutional Layers (RDCL), Multiple Scale Convolutional Layers (MSCL)
 *  intro: the proposed detector runs at 20 FPS on a single CPU core and 125 FPS using a GPU for VGA-resolution images
 *  arxiv: https://arxiv.org/abs/1708.05234
 *  github(official): https://github.com/sfzhang15/FaceBoxes
 *  github(Caffe): https://github.com/zeusees/FaceBoxes

### S3FD: Single Shot Scale-invariant Face Detector ###

*  intro: ICCV 2017. Chinese Academy of Sciences
 *  intro: can run at 36 FPS on a Nvidia Titan X (Pascal) for VGA-resolution images
 *  arxiv: https://arxiv.org/abs/1708.05237
 *  github(Caffe, official): https://github.com/sfzhang15/SFD
 *  github: https://github.com//clcarwin/SFD\_pytorch

### Detecting Faces Using Region-based Fully Convolutional Networks ###

*  arxiv: https://arxiv.org/abs/1709.05256

### AffordanceNet: An End-to-End Deep Learning Approach for Object Affordance Detection ###

*  arxiv: https://arxiv.org/abs/1709.07326

### Face Attention Network: An effective Face Detector for the Occluded Faces ###

*  arxiv: https://arxiv.org/abs/1711.07246

### Feature Agglomeration Networks for Single Stage Face Detection ###

*  arxiv: https://arxiv.org/abs/1712.00721

### Face Detection Using Improved Faster RCNN ###

*  intro: Huawei Cloud BU
 *  arxiv: https://arxiv.org/abs/1802.02142

### PyramidBox: A Context-assisted Single Shot Face Detector ###

*  intro: Baidu, Inc
 *  arxiv: https://arxiv.org/abs/1803.07737

### A Fast Face Detection Method via Convolutional Neural Network ###

*  intro: Neurocomputing
 *  arxiv: https://arxiv.org/abs/1803.10103

### Beyond Trade-off: Accelerate FCN-based Face Detector with Higher Accuracy ###

*  intro: CVPR 2018. Beihang University & CUHK & Sensetime
 *  arxiv: https://arxiv.org/abs/1804.05197

### Real-Time Rotation-Invariant Face Detection with Progressive Calibration Networks ###

*  intro: CVPR 2018
 *  arxiv: https://arxiv.org/abs/1804.06039
 *  github: https://github.com/Jack-CV/PCN

### SFace: An Efficient Network for Face Detection in Large Scale Variations ###

*  intro: Beihang University & Megvii Inc. (Face++)
 *  arxiv: https://arxiv.org/abs/1804.06559

### Survey of Face Detection on Low-quality Images ###

*  arxiv: https://arxiv.org/abs/1804.07362

### Anchor Cascade for Efficient Face Detection ###

*  intro: The University of Sydney
 *  arxiv: https://arxiv.org/abs/1805.03363

### Adversarial Attacks on Face Detectors using Neural Net based Constrained Optimization ###

*  intro: IEEE MMSP
 *  arxiv: https://arxiv.org/abs/1805.12302

### Selective Refinement Network for High Performance Face Detection ###

*  https://arxiv.org/abs/1809.02693

### DSFD: Dual Shot Face Detector ###

*  arxiv：https://arxiv.org/abs/1810.10220

### Learning Better Features for Face Detection with Feature Fusion and Segmentation Supervision ###

*  arxiv：https://arxiv.org/abs/1811.08557

### FA-RPN: Floating Region Proposals for Face Detection ###

*  arxiv: https://arxiv.org/abs/1812.05586

### Robust and High Performance Face Detector ###

https://arxiv.org/abs/1901.02350

### DAFE-FD: Density Aware Feature Enrichment for Face Detection ###

*  arxiv: https://arxiv.org/abs/1901.05375

### Improved Selective Refinement Network for Face Detection ###

*  intro: Chinese Academy of Sciences & JD AI Research
 *  arxiv: https://arxiv.org/abs/1901.06651

### Revisiting a single-stage method for face detection ###

*  arxiv: https://arxiv.org/abs/1902.01559

--------------------

## Detect Small Faces ##

### Finding Tiny Faces ###

*  intro: CVPR 2017. CMU
 *  project page: http://www.cs.cmu.edu/~peiyunh/tiny/index.html
 *  arxiv: https://arxiv.org/abs/1612.04402
 *  github(official, Matlab): https://github.com/peiyunh/tiny
 *  github(inference-only): https://github.com/chinakook/hr101\_mxnet
 *  github: https://github.com/cydonia999/Tiny\_Faces\_in\_Tensorflow

### Detecting and counting tiny faces ###

*  intro: ENS Paris-Saclay. ExtendedTinyFaces
 *  intro: Detecting and counting small objects - Analysis, review and application to counting
 *  arxiv: https://arxiv.org/abs/1801.06504
 *  github: https://github.com/alexattia/ExtendedTinyFaces

### Seeing Small Faces from Robust Anchor’s Perspective ###

*  intro: CVPR 2018
 *  arxiv: https://arxiv.org/abs/1802.09058

### Face-MagNet: Magnifying Feature Maps to Detect Small Faces ###

*  intro: WACV 2018
 *  keywords: Face Magnifier Network (Face-MageNet)
 *  arxiv: https://arxiv.org/abs/1803.05258
 *  github: https://github.com/po0ya/face-magnet

### Robust Face Detection via Learning Small Faces on Hard Images ###

*  intro: Johns Hopkins University & Stanford University
 *  arxiv: https://arxiv.org/abs/1811.11662
 *  github: https://github.com/bairdzhang/smallhardface

### SFA: Small Faces Attention Face Detector ###

*  intro: Jilin University
 *  arxiv: https://arxiv.org/abs/1812.08402

--------------------

## Person Head Detection ##

### Context-aware CNNs for person head detection ###

*  intro: ICCV 2015
 *  project page: http://www.di.ens.fr/willow/research/headdetection/
 *  arxiv: http://arxiv.org/abs/1511.07917
 *  github: https://github.com/aosokin/cnn\_head\_detection

### Detecting Heads using Feature Refine Net and Cascaded Multi-scale Architecture ###

*  arxiv: https://arxiv.org/abs/1803.09256

### A Comparison of CNN-based Face and Head Detectors for Real-Time Video Surveillance Applications ###

*  https://arxiv.org/abs/1809.03336

### FCHD: A fast and accurate head detector ###

*  arxiv: https://arxiv.org/abs/1809.08766
 *  github(PyTorch, official): https://github.com/aditya-vora/FCHD-Fully-Convolutional-Head-Detector

--------------------

## Pedestrian Detection / People Detection ##

### Pedestrian Detection aided by Deep Learning Semantic Tasks ###

*  intro: CVPR 2015
 *  project page: http://mmlab.ie.cuhk.edu.hk/projects/TA-CNN/
 *  arxiv: http://arxiv.org/abs/1412.0069

### Deep Learning Strong Parts for Pedestrian Detection ###

*  intro: ICCV 2015. CUHK. DeepParts
 *  intro: Achieving 11.89% average miss rate on Caltech Pedestrian Dataset
 *  paper: http://personal.ie.cuhk.edu.hk/~pluo/pdf/tianLWTiccv15.pdf

### Taking a Deeper Look at Pedestrians ###

*  intro: CVPR 2015
 *  arxiv: https://arxiv.org/abs/1501.05790

### Convolutional Channel Features ###

*  intro: ICCV 2015
 *  arxiv: https://arxiv.org/abs/1504.07339
 *  github: https://github.com/byangderek/CCF

### End-to-end people detection in crowded scenes ###

*  arxiv: http://arxiv.org/abs/1506.04878
 *  github: https://github.com/Russell91/reinspect
 *  ipn: http://nbviewer.ipython.org/github/Russell91/ReInspect/blob/master/evaluation\_reinspect.ipynb
 *  youtube: https://www.youtube.com/watch?v=QeWl0h3kQ24

### Learning Complexity-Aware Cascades for Deep Pedestrian Detection ###

*  intro: ICCV 2015
 *  arxiv: https://arxiv.org/abs/1507.05348

### Deep convolutional neural networks for pedestrian detection ###

*  arxiv: http://arxiv.org/abs/1510.03608
 *  github: https://github.com/DenisTome/DeepPed

### Scale-aware Fast R-CNN for Pedestrian Detection ###

*  arxiv: https://arxiv.org/abs/1510.08160

### New algorithm improves speed and accuracy of pedestrian detection ###

*  blog: http://www.eurekalert.org/pub\_releases/2016-02/uoc–nai020516.php

### Pushing the Limits of Deep CNNs for Pedestrian Detection ###

*  intro: “set a new record on the Caltech pedestrian dataset, lowering the log-average miss rate from 11.7% to 8.9%”
 *  arxiv: http://arxiv.org/abs/1603.04525

### A Real-Time Deep Learning Pedestrian Detector for Robot Navigation ###

*  arxiv: http://arxiv.org/abs/1607.04436

### A Real-Time Pedestrian Detector using Deep Learning for Human-Aware Navigation ###

*  arxiv: http://arxiv.org/abs/1607.04441

### Is Faster R-CNN Doing Well for Pedestrian Detection? ###

*  intro: ECCV 2016
 *  arxiv: http://arxiv.org/abs/1607.07032
 *  github: https://github.com/zhangliliang/RPN\_BF/tree/RPN-pedestrian

### Unsupervised Deep Domain Adaptation for Pedestrian Detection ###

*  intro: ECCV Workshop 2016
 *  arxiv: https://arxiv.org/abs/1802.03269

### Reduced Memory Region Based Deep Convolutional Neural Network Detection ###

*  intro: IEEE 2016 ICCE-Berlin
 *  arxiv: http://arxiv.org/abs/1609.02500

### Fused DNN: A deep neural network fusion approach to fast and robust pedestrian detection ###

*  arxiv: https://arxiv.org/abs/1610.03466

### Detecting People in Artwork with CNNs ###

*  intro: ECCV 2016 Workshops
 *  arxiv: https://arxiv.org/abs/1610.08871

### Multispectral Deep Neural Networks for Pedestrian Detection ###

*  intro: BMVC 2016 oral
 *  arxiv: https://arxiv.org/abs/1611.02644

### Box-level Segmentation Supervised Deep Neural Networks for Accurate and Real-time Multispectral Pedestrian Detection ###

*  arxiv: https://arxiv.org/abs/1902.05291

### Deep Multi-camera People Detection ###

*  arxiv: https://arxiv.org/abs/1702.04593

### Expecting the Unexpected: Training Detectors for Unusual Pedestrians with Adversarial Imposters ###

*  intro: CVPR 2017
 *  project page: http://ml.cs.tsinghua.edu.cn:5000/publications/synunity/
 *  arxiv: https://arxiv.org/abs/1703.06283
 *  github(Tensorflow): https://github.com/huangshiyu13/RPNplus

### What Can Help Pedestrian Detection? ###

*  intro: CVPR 2017. Tsinghua University & Peking University & Megvii Inc.
 *  keywords: Faster R-CNN, HyperLearner
 *  arxiv: https://arxiv.org/abs/1705.02757
 *  paper: http://openaccess.thecvf.com/content\_cvpr\_2017/papers/Mao\_What\_Can\_Help\_CVPR\_2017\_paper.pdf

### Illuminating Pedestrians via Simultaneous Detection & Segmentation ###

*  arxiv: [https://arxiv.org/abs/1706.08564][https_arxiv.org_abs_1706.08564]

### Rotational Rectification Network for Robust Pedestrian Detection ###

*  intro: CMU & Volvo Construction
 *  arxiv: https://arxiv.org/abs/1706.08917

### STD-PD: Generating Synthetic Training Data for Pedestrian Detection in Unannotated Videos ###

*  intro: The University of North Carolina at Chapel Hill
 *  arxiv: https://arxiv.org/abs/1707.09100

### Too Far to See? Not Really! — Pedestrian Detection with Scale-aware Localization Policy ###

*  arxiv: https://arxiv.org/abs/1709.00235

### Repulsion Loss: Detecting Pedestrians in a Crowd ###

*  arxiv: https://arxiv.org/abs/1711.07752

### Aggregated Channels Network for Real-Time Pedestrian Detection ###

*  arxiv: https://arxiv.org/abs/1801.00476

### Illumination-aware Faster R-CNN for Robust Multispectral Pedestrian Detection ###

*  intro: State Key Lab of CAD&CG, Zhejiang University
 *  arxiv: https://arxiv.org/abs/1803.05347

### Exploring Multi-Branch and High-Level Semantic Networks for Improving Pedestrian Detection ###

*  arxiv: https://arxiv.org/abs/1804.00872

### Pedestrian-Synthesis-GAN: Generating Pedestrian Data in Real Scene and Beyond ###

*  arxiv: https://arxiv.org/abs/1804.02047

### PCN: Part and Context Information for Pedestrian Detection with CNNs ###

*  intro: British Machine Vision Conference(BMVC) 2017
 *  arxiv: https://arxiv.org/abs/1804.04483

### Small-scale Pedestrian Detection Based on Somatic Topology Localization and Temporal Feature Aggregation ###

*  intro: ECCV 2018. Hikvision Research Institute
 *  arxiv: https://arxiv.org/abs/1807.01438

### Occlusion-aware R-CNN: Detecting Pedestrians in a Crowd ###

*  intro: ECCV 2018
 *  arxiv: https://arxiv.org/abs/1807.08407

### Multispectral Pedestrian Detection via Simultaneous Detection and Segmentation ###

*  intro: BMVC 2018
 *  arxiv: https://arxiv.org/abs/1808.04818

### Pedestrian Detection with Autoregressive Network Phases ###

*  intro: Michigan State University
 *  arxiv: https://arxiv.org/abs/1812.00440

### The Cross-Modality Disparity Problem in Multispectral Pedestrian Detection ###

\-arxiv: https://arxiv.org/abs/1901.02645

--------------------

## Vehicle Detection ##

### DAVE: A Unified Framework for Fast Vehicle Detection and Annotation ###

*  intro: ECCV 2016
 *  arxiv: http://arxiv.org/abs/1607.04564

### Evolving Boxes for fast Vehicle Detection ###

*  arxiv: https://arxiv.org/abs/1702.00254

### Fine-Grained Car Detection for Visual Census Estimation ###

*  intro: AAAI 2016
 *  arxiv: https://arxiv.org/abs/1709.02480

### SINet: A Scale-insensitive Convolutional Neural Network for Fast Vehicle Detection ###

*  intro: IEEE Transactions on Intelligent Transportation Systems (T-ITS)
 *  arxiv: https://arxiv.org/abs/1804.00433

### Label and Sample: Efficient Training of Vehicle Object Detector from Sparsely Labeled Data ###

*  intro: UC Berkeley
 *  arxiv: https://arxiv.org/abs/1808.08603

### Domain Randomization for Scene-Specific Car Detection and Pose Estimation ###

*  arxiv：https://arxiv.org/abs/1811.05939

### ShuffleDet: Real-Time Vehicle Detection Network in On-board Embedded UAV Imagery ###

*  intro: ECCV 2018, UAVision 2018
 *  arxiv: https://arxiv.org/abs/1811.06318

--------------------

## Traffic-Sign Detection ##

### Traffic-Sign Detection and Classification in the Wild ###

*  intro: CVPR 2016
 *  project page(code+dataset): http://cg.cs.tsinghua.edu.cn/traffic-sign/
 *  paper: http://www.cv-foundation.org/openaccess/content\_cvpr\_2016/papers/Zhu\_Traffic-Sign\_Detection\_and\_CVPR\_2016\_paper.pdf
 *  code & model: http://cg.cs.tsinghua.edu.cn/traffic-sign/data\_model\_code/newdata0411.zip

### Evaluating State-of-the-art Object Detector on Challenging Traffic Light Data ###

*  intro: CVPR 2017 workshop
 *  paper: http://openaccess.thecvf.com/content\_cvpr\_2017\_workshops/w9/papers/Jensen\_Evaluating\_State-Of-The-Art\_Object\_CVPR\_2017\_paper.pdf

### Detecting Small Signs from Large Images ###

*  intro: IEEE Conference on Information Reuse and Integration (IRI) 2017 oral
 *  arxiv: https://arxiv.org/abs/1706.08574

### Localized Traffic Sign Detection with Multi-scale Deconvolution Networks ###

*  arxiv: https://arxiv.org/abs/1804.10428

### Detecting Traffic Lights by Single Shot Detection ###

*  intro: ITSC 2018
 *  arxiv: https://arxiv.org/abs/1805.02523

### A Hierarchical Deep Architecture and Mini-Batch Selection Method For Joint Traffic Sign and Light Detection ###

*  intro: IEEE 15th Conference on Computer and Robot Vision
 *  arxiv: https://arxiv.org/abs/1806.07987
 *  demo: https://www.youtube.com/watch?v=\_YmogPzBXOw&feature=youtu.be

--------------------

## Skeleton Detection ##

### Object Skeleton Extraction in Natural Images by Fusing Scale-associated Deep Side Outputs ###

![这里写图片描述][70 7]

*  arxiv: http://arxiv.org/abs/1603.09446
 *  github: https://github.com/zeakey/DeepSkeleton

### DeepSkeleton: Learning Multi-task Scale-associated Deep Side Outputs for Object Skeleton Extraction in Natural Images ###

*  arxiv: http://arxiv.org/abs/1609.03659

### SRN: Side-output Residual Network for Object Symmetry Detection in the Wild ###

*  intro: CVPR 2017
 *  arxiv: https://arxiv.org/abs/1703.02243
 *  github: https://github.com/KevinKecc/SRN

### Hi-Fi: Hierarchical Feature Integration for Skeleton Detection ###

*  arxiv: https://arxiv.org/abs/1801.01849

--------------------

## Fruit Detection ##

### Deep Fruit Detection in Orchards ###

*  arxiv: https://arxiv.org/abs/1610.03677

### Image Segmentation for Fruit Detection and Yield Estimation in Apple Orchards ###

*  intro: The Journal of Field Robotics in May 2016
 *  project page: http://confluence.acfr.usyd.edu.au/display/AGPub/
 *  arxiv: https://arxiv.org/abs/1610.08120

--------------------

## Shadow Detection ##

### Fast Shadow Detection from a Single Image Using a Patched Convolutional Neural Network ###

*  arxiv: https://arxiv.org/abs/1709.09283

### A+D-Net: Shadow Detection with Adversarial Shadow Attenuation ###

*  arxiv: https://arxiv.org/abs/1712.01361

### Stacked Conditional Generative Adversarial Networks for Jointly Learning Shadow Detection and Shadow Removal ###

*  arxiv: https://arxiv.org/abs/1712.02478

### Direction-aware Spatial Context Features for Shadow Detection ###

*  intro: CVPR 2018
 *  arxiv: https://arxiv.org/abs/1712.04142

### Direction-aware Spatial Context Features for Shadow Detection and Removal ###

*  intro: The Chinese University of Hong Kong & The Hong Kong Polytechnic University
 *  arxiv: https://arxiv.org/abs/1805.04635

--------------------

## Others Detection ##

### Deep Deformation Network for Object Landmark Localization ###

*  arxiv: http://arxiv.org/abs/1605.01014

### Fashion Landmark Detection in the Wild ###

*  intro: ECCV 2016
 *  project page: http://personal.ie.cuhk.edu.hk/~lz013/projects/FashionLandmarks.html
 *  arxiv: http://arxiv.org/abs/1608.03049
 *  github(Caffe): https://github.com/liuziwei7/fashion-landmarks

### Deep Learning for Fast and Accurate Fashion Item Detection ###

*  intro: Kuznech Inc.
 *  intro: MultiBox and Fast R-CNN
 *  paper: https://kddfashion2016.mybluemix.net/kddfashion\_finalSubmissions/Deep%20Learning%20for%20Fast%20and%20Accurate%20Fashion%20Item%20Detection.pdf

### OSMDeepOD - OSM and Deep Learning based Object Detection from Aerial Imagery (formerly known as “OSM-Crosswalk-Detection”) ###

![这里写图片描述][70 8]

*  github: https://github.com/geometalab/OSMDeepOD

### Selfie Detection by Synergy-Constraint Based Convolutional Neural Network ###

*  intro: IEEE SITIS 2016
 *  arxiv: https://arxiv.org/abs/1611.04357

### Associative Embedding:End-to-End Learning for Joint Detection and Grouping ###

*  arxiv: https://arxiv.org/abs/1611.05424

### Deep Cuboid Detection: Beyond 2D Bounding Boxes ###

*  intro: CMU & Magic Leap
 *  arxiv: https://arxiv.org/abs/1611.10010

### Automatic Model Based Dataset Generation for Fast and Accurate Crop and Weeds Detection ###

*  arxiv: https://arxiv.org/abs/1612.03019

### Deep Learning Logo Detection with Data Expansion by Synthesising Context ###

*  arxiv: https://arxiv.org/abs/1612.09322

### Scalable Deep Learning Logo Detection ###

*  arxiv: https://arxiv.org/abs/1803.11417

### Pixel-wise Ear Detection with Convolutional Encoder-Decoder Networks ###

*  arxiv: https://arxiv.org/abs/1702.00307

### Automatic Handgun Detection Alarm in Videos Using Deep Learning ###

*  arxiv: https://arxiv.org/abs/1702.05147
 *  results: https://github.com/SihamTabik/Pistol-Detection-in-Videos

### Objects as context for part detection ###

*  arxiv: https://arxiv.org/abs/1703.09529

### Using Deep Networks for Drone Detection ###

*  intro: AVSS 2017
 *  arxiv: https://arxiv.org/abs/1706.05726

### Cut, Paste and Learn: Surprisingly Easy Synthesis for Instance Detection ###

*  intro: ICCV 2017
 *  arxiv: https://arxiv.org/abs/1708.01642

### Target Driven Instance Detection ###

*  arxiv: https://arxiv.org/abs/1803.04610

### DeepVoting: An Explainable Framework for Semantic Part Detection under Partial Occlusion ###

*  arxiv: https://arxiv.org/abs/1709.04577

### VPGNet: Vanishing Point Guided Network for Lane and Road Marking Detection and Recognition ###

*  intro: ICCV 2017
 *  arxiv: https://arxiv.org/abs/1710.06288
 *  github: https://github.com/SeokjuLee/VPGNet

### Grab, Pay and Eat: Semantic Food Detection for Smart Restaurants ###

*  arxiv: https://arxiv.org/abs/1711.05128

### ReMotENet: Efficient Relevant Motion Event Detection for Large-scale Home Surveillance Videos ###

*  intro: WACV 2018
 *  arxiv: https://arxiv.org/abs/1801.02031

### Deep Learning Object Detection Methods for Ecological Camera Trap Data ###

*  intro: Conference of Computer and Robot Vision. University of Guelph
 *  arxiv: https://arxiv.org/abs/1803.10842

### EL-GAN: Embedding Loss Driven Generative Adversarial Networks for Lane Detection ###

*  arxiv: https://arxiv.org/abs/1806.05525

### Towards End-to-End Lane Detection: an Instance Segmentation Approach ###

*  arxiv: https://arxiv.org/abs/1802.05591
 *  github: https://github.com/MaybeShewill-CV/lanenet-lane-detection

### iCAN: Instance-Centric Attention Network for Human-Object Interaction Detection ###

*  intro: BMVC 2018
 *  project page: https://gaochen315.github.io/iCAN/
 *  arxiv: https://arxiv.org/abs/1808.10437
 *  github: https://github.com/vt-vl-lab/iCAN

### Densely Supervised Grasp Detector (DSGD) ###

*  https://arxiv.org/abs/1810.03962

--------------------

## Object Proposal ##

### DeepProposal: Hunting Objects by Cascading Deep Convolutional Layers ###

*  arxiv: http://arxiv.org/abs/1510.04445
 *  github: https://github.com/aghodrati/deepproposal

### Scale-aware Pixel-wise Object Proposal Networks ###

*  intro: IEEE Transactions on Image Processing
 *  arxiv: http://arxiv.org/abs/1601.04798

### Attend Refine Repeat: Active Box Proposal Generation via In-Out Localization ###

*  intro: BMVC 2016. AttractioNet
 *  arxiv: https://arxiv.org/abs/1606.04446
 *  github: https://github.com/gidariss/AttractioNet

### Learning to Segment Object Proposals via Recursive Neural Networks ###

*  arxiv: https://arxiv.org/abs/1612.01057

### Learning Detection with Diverse Proposals ###

*  intro: CVPR 2017
 *  keywords: differentiable Determinantal Point Process (DPP) layer, Learning Detection with Diverse Proposals (LDDP)
 *  arxiv: https://arxiv.org/abs/1704.03533

### ScaleNet: Guiding Object Proposal Generation in Supermarkets and Beyond ###

*  keywords: product detection
 *  arxiv: https://arxiv.org/abs/1704.06752

### Improving Small Object Proposals for Company Logo Detection ###

*  intro: ICMR 2017
 *  arxiv: https://arxiv.org/abs/1704.08881

### Open Logo Detection Challenge ###

*  intro: BMVC 2018
 *  keywords: QMUL-OpenLogo
 *  project page: https://qmul-openlogo.github.io/
 *  arxiv: https://arxiv.org/abs/1807.01964

### AttentionMask: Attentive, Efficient Object Proposal Generation Focusing on Small Objects ###

*  intro: ACCV 2018 oral
 *  arxiv: https://arxiv.org/abs/1811.08728
 *  github: https://github.com/chwilms/AttentionMask

--------------------

## Localization ##

### Beyond Bounding Boxes: Precise Localization of Objects in Images ###

*  intro: PhD Thesis
 *  homepage: http://www.eecs.berkeley.edu/Pubs/TechRpts/2015/EECS-2015-193.html
 *  phd-thesis: http://www.eecs.berkeley.edu/Pubs/TechRpts/2015/EECS-2015-193.pdf
 *  github(“SDS using hypercolumns”): https://github.com/bharath272/sds

### Weakly Supervised Object Localization with Multi-fold Multiple Instance Learning ###

*  arxiv: http://arxiv.org/abs/1503.00949

### Weakly Supervised Object Localization Using Size Estimates ###

*  arxiv: http://arxiv.org/abs/1608.04314

### Active Object Localization with Deep Reinforcement Learning ###

*  intro: ICCV 2015
 *  keywords: Markov Decision Process
 *  arxiv: https://arxiv.org/abs/1511.06015

### Localizing objects using referring expressions ###

*  intro: ECCV 2016
 *  keywords: LSTM, multiple instance learning (MIL)
 *  paper: http://www.umiacs.umd.edu/~varun/files/refexp-ECCV16.pdf
 *  github: https://github.com/varun-nagaraja/referring-expressions

### LocNet: Improving Localization Accuracy for Object Detection ###

*  intro: CVPR 2016 oral
 *  arxiv: http://arxiv.org/abs/1511.07763
 *  github: https://github.com/gidariss/LocNet

### Learning Deep Features for Discriminative Localization ###

![这里写图片描述][70 9]

*  homepage: http://cnnlocalization.csail.mit.edu/
 *  arxiv: http://arxiv.org/abs/1512.04150
 *  github(Tensorflow): https://github.com/jazzsaxmafia/Weakly\_detector
 *  github: https://github.com/metalbubble/CAM
 *  github: https://github.com/tdeboissiere/VGG16CAM-keras

### ContextLocNet: Context-Aware Deep Network Models for Weakly Supervised Localization ###

![这里写图片描述][70 10]

*  intro: ECCV 2016
 *  project page: http://www.di.ens.fr/willow/research/contextlocnet/
 *  arxiv: http://arxiv.org/abs/1609.04331
 *  github: https://github.com/vadimkantorov/contextlocnet

### Ensemble of Part Detectors for Simultaneous Classification and Localization ###

*  arxiv: https://arxiv.org/abs/1705.10034

### STNet: Selective Tuning of Convolutional Networks for Object Localization ###

*  arxiv: https://arxiv.org/abs/1708.06418

### Soft Proposal Networks for Weakly Supervised Object Localization ###

*  intro: ICCV 2017
 *  arxiv: https://arxiv.org/abs/1709.01829

### Fine-grained Discriminative Localization via Saliency-guided Faster R-CNN ###

*  intro: ACM MM 2017
 *  arxiv: https://arxiv.org/abs/1709.08295

--------------------

## Tutorials / Talks ##

### Convolutional Feature Maps: Elements of efficient (and accurate) CNN-based object detection ###

*  slides: http://research.microsoft.com/en-us/um/people/kahe/iccv15tutorial/iccv2015\_tutorial\_convolutional\_feature\_maps\_kaiminghe.pdf

### Towards Good Practices for Recognition & Detection ###

*  intro: Hikvision Research Institute. Supervised Data Augmentation (SDA)
 *  slides: http://image-net.org/challenges/talks/2016/Hikvision\_at\_ImageNet\_2016.pdf

### Work in progress: Improving object detection and instance segmentation for small objects ###

[https://docs.google.com/presentation/d/1OTfGn6mLe1VWE8D0q6Tu\_WwFTSoLGd4OF8WCYnOWcVo/edit\#slide=id.g37418adc7a\_0\_229][https_docs.google.com_presentation_d_1OTfGn6mLe1VWE8D0q6Tu_WwFTSoLGd4OF8WCYnOWcVo_edit_slide_id.g37418adc7a_0_229]

### Object Detection with Deep Learning: A Review ###

*  arxiv: https://arxiv.org/abs/1807.05511

--------------------

## Projects ##

### Detectron ###

*  intro: FAIR’s research platform for object detection research, implementing popular algorithms like Mask R-CNN and RetinaNet.
 *  github: https://github.com/facebookresearch/Detectron

### TensorBox: a simple framework for training neural networks to detect objects in images ###

*  intro: “The basic model implements the simple and robust GoogLeNet-OverFeat algorithm. We additionally provide an implementation of the ReInspect algorithm”
 *  github: https://github.com/Russell91/TensorBox

### Object detection in torch: Implementation of some object detection frameworks in torch ###

*  github: https://github.com/fmassa/object-detection.torch

### Using DIGITS to train an Object Detection network ###

*  github: https://github.com/NVIDIA/DIGITS/blob/master/examples/object-detection/README.md

### FCN-MultiBox Detector ###

*  intro: Full convolution MultiBox Detector (like SSD) implemented in Torch.
 *  github: https://github.com/teaonly/FMD.torch

### KittiBox: A car detection model implemented in Tensorflow. ###

*  keywords: MultiNet
 *  intro: KittiBox is a collection of scripts to train out model FastBox on the Kitti Object Detection Dataset
 *  github: https://github.com/MarvinTeichmann/KittiBox

### Deformable Convolutional Networks + MST + Soft-NMS ###

*  github: https://github.com/bharatsingh430/Deformable-ConvNets

### How to Build a Real-time Hand-Detector using Neural Networks (SSD) on Tensorflow ###

*  blog: https://towardsdatascience.com/how-to-build-a-real-time-hand-detector-using-neural-networks-ssd-on-tensorflow-d6bac0e4b2ce
 *  github: https://github.com//victordibia/handtracking

### Metrics for object detection ###

*  intro: Most popular metrics used to evaluate object detection algorithms
 *  github: https://github.com/rafaelpadilla/Object-Detection-Metrics

### MobileNetv2-SSDLite ###

*  intro: Caffe implementation of SSD and SSDLite detection on MobileNetv2, converted from tensorflow.
 *  github: https://github.com/chuanqi305/MobileNetv2-SSDLite

--------------------

## Leaderboard ##

### Detection Results: VOC2012 ###

*  intro: Competition “comp4” (train on additional data)
 *  homepage: http://host.robots.ox.ac.uk:8080/leaderboard/displaylb.php?challengeid=11&compid=4

--------------------

## Tools ##

### BeaverDam: Video annotation tool for deep learning training labels ###

https://github.com/antingshen/BeaverDam

--------------------

## Blogs ##

### Convolutional Neural Networks for Object Detection ###

http://rnd.azoft.com/convolutional-neural-networks-object-detection/

### Introducing automatic object detection to visual search (Pinterest) ###

*  keywords: Faster R-CNN
 *  blog: https://engineering.pinterest.com/blog/introducing-automatic-object-detection-visual-search
 *  demo: https://engineering.pinterest.com/sites/engineering/files/Visual%20Search%20V1%20-%20Video.mp4
 *  review: https://news.developer.nvidia.com/pinterest-introduces-the-future-of-visual-search/?mkt\_tok=eyJpIjoiTnpaa01UWXpPRE0xTURFMiIsInQiOiJJRjcybjkwTmtmallORUhLOFFFODBDclFqUlB3SWlRVXJXb1MrQ013TDRIMGxLQWlBczFIeWg0TFRUdnN2UHY2ZWFiXC9QQVwvQzBHM3B0UzBZblpOSmUyU1FcLzNPWXI4cml2VERwTTJsOFwvOEk9In0%3D

### Deep Learning for Object Detection with DIGITS ###

*  blog: https://devblogs.nvidia.com/parallelforall/deep-learning-object-detection-digits/

### Analyzing The Papers Behind Facebook’s Computer Vision Approach ###

*  keywords: DeepMask, SharpMask, MultiPathNet
 *  blog: https://adeshpande3.github.io/adeshpande3.github.io/Analyzing-the-Papers-Behind-Facebook’s-Computer-Vision-Approach/

### Easily Create High Quality Object Detectors with Deep Learning ###

*  intro: dlib v19.2
 *  blog: http://blog.dlib.net/2016/10/easily-create-high-quality-object.html

### How to Train a Deep-Learned Object Detection Model in the Microsoft Cognitive Toolkit ###

*  blog: https://blogs.technet.microsoft.com/machinelearning/2016/10/25/how-to-train-a-deep-learned-object-detection-model-in-cntk/
 *  github: https://github.com/Microsoft/CNTK/tree/master/Examples/Image/Detection/FastRCNN

### Object Detection in Satellite Imagery, a Low Overhead Approach ###

*  part 1: https://medium.com/the-downlinq/object-detection-in-satellite-imagery-a-low-overhead-approach-part-i-cbd96154a1b7\#.2csh4iwx9
 *  part 2: https://medium.com/the-downlinq/object-detection-in-satellite-imagery-a-low-overhead-approach-part-ii-893f40122f92\#.f9b7dgf64

### You Only Look Twice — Multi-Scale Object Detection in Satellite Imagery With Convolutional Neural Networks ###

*  part 1: https://medium.com/the-downlinq/you-only-look-twice-multi-scale-object-detection-in-satellite-imagery-with-convolutional-neural-38dad1cf7571\#.fmmi2o3of
 *  part 2: https://medium.com/the-downlinq/you-only-look-twice-multi-scale-object-detection-in-satellite-imagery-with-convolutional-neural-34f72f659588\#.nwzarsz1t

### Faster R-CNN Pedestrian and Car Detection ###

*  blog: https://bigsnarf.wordpress.com/2016/11/07/faster-r-cnn-pedestrian-and-car-detection/
 *  ipn: https://gist.github.com/bigsnarfdude/2f7b2144065f6056892a98495644d3e0\#file-demo\_faster\_rcnn\_notebook-ipynb
 *  github: https://github.com/bigsnarfdude/Faster-RCNN\_TF

### Small U-Net for vehicle detection ###

*  blog: https://medium.com/@vivek.yadav/small-u-net-for-vehicle-detection-9eec216f9fd6\#.md4u80kad

### Region of interest pooling explained ###

*  blog: https://deepsense.io/region-of-interest-pooling-explained/
 *  github: https://github.com/deepsense-io/roi-pooling

### Supercharge your Computer Vision models with the TensorFlow Object Detection API ###

*  blog: https://research.googleblog.com/2017/06/supercharge-your-computer-vision-models.html
 *  github: https://github.com/tensorflow/models/tree/master/object\_detection

### Understanding SSD MultiBox — Real-Time Object Detection In Deep Learning ###

https://towardsdatascience.com/understanding-ssd-multibox-real-time-object-detection-in-deep-learning-495ef744fab

### One-shot object detection ###

http://machinethink.net/blog/object-detection/

### An overview of object detection: one-stage methods ###

https://www.jeremyjordan.me/object-detection-one-stage/

## deep learning object detection ##

*  intro: A paper list of object detection using deep learning.
 *  github: https://github.com/hoya012/deep\_learning\_object\_detection

[https_handong1587.github.io_deep_learning_2015_10_09_object-detection.html]: https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html
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[70 6]: /images/20220516/45a6b582feb14e4995ae9f9b1e6efeb5.png
[https_arxiv.org_abs_1706.08564]: https://arxiv.org/abs/1706.08564
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[https_docs.google.com_presentation_d_1OTfGn6mLe1VWE8D0q6Tu_WwFTSoLGd4OF8WCYnOWcVo_edit_slide_id.g37418adc7a_0_229]: https://docs.google.com/presentation/d/1OTfGn6mLe1VWE8D0q6Tu_WwFTSoLGd4OF8WCYnOWcVo/edit#slide=id.g37418adc7a_0_229