深度学习之视频入门经典+视频分类+视频行为识别+行为检测+视频事件识别+事件检测--附带源码和作者主页

以你之姓@ 2022-04-15 06:26 1491阅读 0赞

# 视频研究入门经典 #

*  ### **Labor-Free Video Concept Learningby Jointly Exploiting Web Videos and Images** ###

intro: CVPR 2016  
    intro: Lead–Exceed Neural Network (LENN), LSTM  
    paper: https://www.microsoft.com/en-us/research/wp-content/uploads/2016/06/CVPR16\_webly\_final.pdf

*  ### Video Fill in the Blank with Merging LSTMs ###

intro: for Large Scale Movie Description and Understanding Challenge (LSMDC) 2016, "Movie fill-in-the-blank" Challenge, UCF\_CRCV  
    intro: Video-Fill-in-the-Blank (ViFitB)  
    arxiv: https://arxiv.org/abs/1610.04062

*  ### Video Pixel Networks ###

intro: Google DeepMind  
    arxiv: https://arxiv.org/abs/1610.00527

*  ### Robust Video Synchronization using Unsupervised Deep Learning ###

arxiv: https://arxiv.org/abs/1610.05985

*  ### Video Propagation Networks ###

intro: CVPR 2017. Max Planck Institute for Intelligent Systems & Bernstein Center for Computational Neuroscience  
    project page: https://varunjampani.github.io/vpn/  
    arxiv: https://arxiv.org/abs/1612.05478  
    github(Caffe): https://github.com/varunjampani/video\_prop\_networks

*  ### Video Frame Synthesis using Deep Voxel Flow ###

project page: https://liuziwei7.github.io/projects/VoxelFlow.html  
    arxiv: https://arxiv.org/abs/1702.02463

*  ### Optimizing Deep CNN-Based Queries over Video Streams at Scale ###

intro: Stanford InfoLab  
    keywords: NoScope. difference detectors, specialized models  
    arxiv: https://arxiv.org/abs/1703.02529  
    github: https://github.com/stanford-futuredata/noscope  
    github: https://github.com/stanford-futuredata/tensorflow-noscope

*  ### NoScope: 1000x Faster Deep Learning Queries over Video ###

http://dawn.cs.stanford.edu/2017/06/22/noscope/

*  ### Unsupervised Visual-Linguistic Reference Resolution in Instructional Videos ###

intro: CVPR 2017. Stanford University & University of Southern California  
    arxiv: https://arxiv.org/abs/1703.02521

*  ### ProcNets: Learning to Segment Procedures in Untrimmed and Unconstrained Videos ###

https://arxiv.org/abs/1703.09788

*  ### Unsupervised Learning Layers for Video Analysis ###

intro: Baidu Research  
    intro: "The experiments demonstrated the potential applications of UL layers and online learning algorithm to head orientation estimation and moving object localization"  
    arxiv: https://arxiv.org/abs/1705.08918

*  ### Look, Listen and Learn ###

intro: DeepMind  
    intro: "Audio-Visual Correspondence" learning  
    arxiv: https://arxiv.org/abs/1705.08168

*  ### Video Imagination from a Single Image with Transformation Generation ###

intro: Peking University  
    arxiv: https://arxiv.org/abs/1706.04124  
    github: https://github.com/gitpub327/VideoImagination

*  ### Learning to Learn from Noisy Web Videos ###

intro: CVPR 2017. Stanford University & CMU & Simon Fraser University  
    arxiv: https://arxiv.org/abs/1706.02884

*  ### Convolutional Long Short-Term Memory Networks for Recognizing First Person Interactions ###

intro: Accepted on the second International Workshop on Egocentric Perception, Interaction and Computing(EPIC) at International Conference on Computer Vision(ICCV-17)  
    arxiv: https://arxiv.org/abs/1709.06495

*  ### Learning Binary Residual Representations for Domain-specific Video Streaming ###

intro: AAAI 2018  
    project page: http://research.nvidia.com/publication/2018-02\_Learning-Binary-Residual  
    arxiv: https://arxiv.org/abs/1712.05087

*  ### Video Representation Learning Using Discriminative Pooling ###

intro: CVPR 2018  
    arxiv: https://arxiv.org/abs/1803.10628

*  ### Rethinking the Faster R-CNN Architecture for Temporal Action Localization ###

intro: CVPR 2018  
    arxiv: https://arxiv.org/abs/1804.07667

*  ### Deep Keyframe Detection in Human Action Videos ###

intro: two-stream ConvNet  
    arxiv: https://arxiv.org/abs/1804.10021

*  ### FFNet: Video Fast-Forwarding via Reinforcement Learning ###

intro: CVPR 2018  
    arxiv: https://arxiv.org/abs/1805.02792

*  ### Fast forwarding Egocentric Videos by Listening and Watching ###

https://arxiv.org/abs/1806.04620

*  ### Scanner: Efficient Video Analysis at Scale ###

intro: CMU  
    arxiv: https://arxiv.org/abs/1805.07339

*  ### Massively Parallel Video Networks ###

intro: DeepMind & University of Oxford  
    arxiv: https://arxiv.org/abs/1806.03863

*  ### Object Level Visual Reasoning in Videos ###

intro: LIRIS & Facebook AI Research  
    arxiv: https://arxiv.org/abs/1806.06157

*  ### Video Time: Properties, Encoders and Evaluation ###

intro: BMVC 2018  
    arxiv: https://arxiv.org/abs/1807.06980

# 视频分类 #

*  ### Large-scale Video Classification with Convolutional Neural Networks ###

intro: CVPR 2014  
    project page: http://cs.stanford.edu/people/karpathy/deepvideo/  
    paper: www.cv-foundation.org/openaccess/content\_cvpr\_2014/papers/Karpathy\_Large-scale\_Video\_Classification\_2014\_CVPR\_paper.pdf

*  ### Exploiting Image-trained CNN Architectures for Unconstrained Video Classification ###

intro: Video-level event detection. extracting deep features for each frame, averaging frame-level deep features  
    arxiv: http://arxiv.org/abs/1503.04144

*  ### Beyond Short Snippets: Deep Networks for Video Classification ###

intro: CNN + LSTM  
    arxiv: http://arxiv.org/abs/1503.08909  
    demo: http://pan.baidu.com/s/1eQ9zLZk

*  ### Modeling Spatial-Temporal Clues in a Hybrid Deep Learning Framework for Video Classification ###

intro: ACM Multimedia, 2015  
    arxiv: http://arxiv.org/abs/1504.01561

### Video Content Recognition with Deep Learning ###

author: Zuxuan Wu, Fudan University  
    slides: http://vision.ouc.edu.cn/valse/slides/20160420/Zuxuan%20Wu%20-%20Video%20Content%20Recognition%20with%20Deep%20Learning-Zuxuan%20Wu.pdf

*  ### Video Content Recognition with Deep Learning ###

author: Yu-Gang Jiang, Lab for Big Video Data Analytics (BigVid), Fudan University  
    slides: http://www.yugangjiang.info/slides/DeepVideoTalk-2015.pdf

*  ### Efficient Large Scale Video Classification ###

intro: Google  
    arxiv: http://arxiv.org/abs/1505.06250

*  ### Fusing Multi-Stream Deep Networks for Video Classification ###

arxiv: http://arxiv.org/abs/1509.06086

*  ### Learning End-to-end Video Classification with Rank-Pooling ###

paper: http://jmlr.org/proceedings/papers/v48/fernando16.html  
    paper: http://jmlr.csail.mit.edu/proceedings/papers/v48/fernando16.pdf  
    summary(by Hugo Larochelle): http://www.shortscience.org/paper?bibtexKey=conf/icml/FernandoG16\#hlarochelle

*  ### Deep Learning for Video Classification and Captioning ###

arxiv: http://arxiv.org/abs/1609.06782

*  ### Fast Video Classification via Adaptive Cascading of Deep Models ###

arxiv: https://arxiv.org/abs/1611.06453

*  ### Deep Feature Flow for Video Recognition ###

intro: CVPR 2017  
    intro: It provides a simple, fast, accurate, and end-to-end framework for video recognition (e.g., object detection and semantic segmentation in videos)  
    arxiv: https://arxiv.org/abs/1611.07715  
    github(official, MXNet): https://github.com/msracver/Deep-Feature-Flow  
    youtube: https://www.youtube.com/watch?v=J0rMHE6ehGw

*  ### Large-Scale YouTube-8M Video Understanding with Deep Neural Networks ###

https://arxiv.org/abs/1706.04488

*  ### Deep Learning Methods for Efficient Large Scale Video Labeling ###

intro: Solution to the Kaggle's competition Google Cloud & YouTube-8M Video Understanding Challenge  
    arxiv: https://arxiv.org/abs/1706.04572  
    github: https://github.com/mpekalski/Y8M

*  ### Learnable pooling with Context Gating for video classification ###

intro: CVPR17 Youtube 8M workshop. Kaggle 1st place  
    arxiv: https://arxiv.org/abs/1706.06905  
    github: https://github.com/antoine77340/LOUPE

*  ### Aggregating Frame-level Features for Large-Scale Video Classification ###

intro: Youtube-8M Challenge, 4th place  
    arxiv: https://arxiv.org/abs/1707.00803

*  ### Tensor-Train Recurrent Neural Networks for Video Classification ###

https://arxiv.org/abs/1707.01786

*  ### Hierarchical Deep Recurrent Architecture for Video Understanding ###

intro: Classification Challenge Track paper in CVPR 2017 Workshop on YouTube-8M Large-Scale Video Understanding  
    arxiv: https://arxiv.org/abs/1707.03296

*  ### Large-scale Video Classification guided by Batch Normalized LSTM Translator ###

intro: CVPR2017 Workshop on Youtube-8M Large-scale Video Understanding  
    arxiv: https://arxiv.org/abs/1707.04045

*  ### UTS submission to Google YouTube-8M Challenge 2017 ###

intro: CVPR'17 Workshop on YouTube-8M  
    arxiv: https://arxiv.org/abs/1707.04143  
    github: https://github.com/ffmpbgrnn/yt8m

*  ### A spatiotemporal model with visual attention for video classification ###

https://arxiv.org/abs/1707.02069

*  ### Cultivating DNN Diversity for Large Scale Video Labelling ###

intro: CVPR 2017 Youtube-8M Workshop  
    arxiv: https://arxiv.org/abs/1707.04272

*  ### Attention Transfer from Web Images for Video Recognition ###

intro: ACM Multimedia, 2017  
    arxiv: https://arxiv.org/abs/1708.00973

*  ### Non-local Neural Networks ###

intro: CVPR 2018. CMU & Facebook AI Research  
    arxiv: https://arxiv.org/abs/1711.07971  
    github(Caffe2): https://github.com/facebookresearch/video-nonlocal-net

*  ### Temporal 3D ConvNets: New Architecture and Transfer Learning for Video Classification ###

https://arxiv.org/abs/1711.08200

*  ### Appearance-and-Relation Networks for Video Classification ###

arxiv: https://arxiv.org/abs/1711.09125  
    github: https://github.com/wanglimin/ARTNet

*  ### Rethinking Spatiotemporal Feature Learning: Speed-Accuracy Trade-offs in Video Classification ###

intro: ECCV 2018. Google Research & University of California San Diego  
    arxiv: https://arxiv.org/abs/1712.04851

*  ### Long Activity Video Understanding using Functional Object-Oriented Network ###

https://arxiv.org/abs/1807.00983

Deep Architectures and Ensembles for Semantic Video Classification

https://arxiv.org/abs/1807.01026

*  ### Deep Discriminative Model for Video Classification ###

intro: ECCV 2018  
    arxiv: https://arxiv.org/abs/1807.08259

*  ### Deep Video Color Propagation ###

intro: BMVC 2018  
    arxuv: https://arxiv.org/abs/1808.03232

*  ### Non-local NetVLAD Encoding for Video Classification ###

intro: ECCV 2018 workshop on YouTube-8M Large-Scale Video Understanding  
    intro: Tencent AI Lab & Fudan University  
    arxiv: https://arxiv.org/abs/1810.00207

### Learnable Pooling Methods for Video Classification ###

intro: Youtube 8M ECCV18 Workshop  
    arxiv: https://arxiv.org/abs/1810.00530  
    github: https://github.com/pomonam/LearnablePoolingMethods

*  ### NeXtVLAD: An Efficient Neural Network to Aggregate Frame-level Features for Large-scale Video Classification ###

intro: ECCV 2018 workshop  
    arxiv: https://arxiv.org/abs/1811.05014  
    github: https://github.com/linrongc/youtube-8m

# 视频行为识别 / 行为检测 #

*  ### 3d convolutional neural networks for human action recognition ###

paper: http://www.cs.odu.edu/~sji/papers/pdf/Ji\_ICML10.pdf

*  ### Sequential Deep Learning for Human Action Recognition ###

paper: http://liris.cnrs.fr/Documents/Liris-5228.pdf

*  ### Two-stream convolutional networks for action recognition in videos ###

arxiv: http://arxiv.org/abs/1406.2199

*  ### Finding action tubes ###

intro: "built action models from shape and motion cues. They start from the image proposals and select the motion salient subset of them and extract saptio-temporal features to represent the video using the CNNs."  
    arxiv: http://arxiv.org/abs/1411.6031

*  ### Hierarchical Recurrent Neural Network for Skeleton Based Action Recognition ###

paper: http://www.cv-foundation.org/openaccess/content\_cvpr\_2015/papers/Du\_Hierarchical\_Recurrent\_Neural\_2015\_CVPR\_paper.pdf

*  ### Action Recognition with Trajectory-Pooled Deep-Convolutional Descriptors ###

intro: CVPR 2015. TDD  
    paper: www.cv-foundation.org/openaccess/content\_cvpr\_2015/papers/Wang\_Action\_Recognition\_With\_2015\_CVPR\_paper.pdf  
    ext: http://www.cv-foundation.org/openaccess/content\_cvpr\_2015/app/2B\_105\_ext.pdf  
    poster: https://wanglimin.github.io/papers/WangQT\_CVPR15\_Poster.pdf  
    github: https://github.com/wanglimin/TDD

*  ### Action Recognition by Hierarchical Mid-level Action Elements ###

paper: http://cvgl.stanford.edu/papers/tian2015.pdf

Contextual Action Recognition with R\*CNN

arxiv: http://arxiv.org/abs/1505.01197  
    github: https://github.com/gkioxari/RstarCNN

*  ### Towards Good Practices for Very Deep Two-Stream ConvNets ###

arxiv: http://arxiv.org/abs/1507.02159  
    github: https://github.com/yjxiong/caffe

*  ### Action Recognition using Visual Attention ###

intro: LSTM / RNN  
    arxiv: http://arxiv.org/abs/1511.04119  
    project page: http://shikharsharma.com/projects/action-recognition-attention/  
    github(Python/Theano): https://github.com/kracwarlock/action-recognition-visual-attention

*  ### End-to-end Learning of Action Detection from Frame Glimpses in Videos ###

intro: CVPR 2016  
    project page: http://ai.stanford.edu/~syyeung/frameglimpses.html  
    arxiv: http://arxiv.org/abs/1511.06984  
    paper: http://vision.stanford.edu/pdf/yeung2016cvpr.pdf

*  ### Multi-velocity neural networks for gesture recognition in videos ###

arxiv: http://arxiv.org/abs/1603.06829

Active Learning for Online Recognition of Human Activities from Streaming Videos

arxiv: http://arxiv.org/abs/1604.02855

*  ### Convolutional Two-Stream Network Fusion for Video Action Recognition ###

arxiv: http://arxiv.org/abs/1604.06573  
    github: https://github.com/feichtenhofer/twostreamfusion

*  ### Deep, Convolutional, and Recurrent Models for Human Activity Recognition using Wearables ###

arxiv: http://arxiv.org/abs/1604.08880

*  ### Unsupervised Semantic Action Discovery from Video Collections ###

arxiv: http://arxiv.org/abs/1605.03324

*  ### Anticipating Visual Representations from Unlabeled Video ###

paper: http://web.mit.edu/vondrick/prediction.pdf

VideoLSTM Convolves, Attends and Flows for Action Recognition

arxiv: http://arxiv.org/abs/1607.01794

*  ### Hierarchical Attention Network for Action Recognition in Videos (HAN) ###

arxiv: http://arxiv.org/abs/1607.06416

*  ### Spatio-Temporal LSTM with Trust Gates for 3D Human Action Recognition ###

arxiv: http://arxiv.org/abs/1607.07043

*  ### Connectionist Temporal Modeling for Weakly Supervised Action Labeling ###

arxiv: http://arxiv.org/abs/1607.08584

*  ### CUHK & ETHZ & SIAT Submission to ActivityNet Challenge 2016 ###

intro: won the 1st place in the untrimmed video classification task of ActivityNet Challenge 2016. TSN  
    arxiv: http://arxiv.org/abs/1608.00797  
    github: https://github.com/yjxiong/anet2016-cuhk

*  ### Actionness Estimation Using Hybrid FCNs ###

intro: CVPR 2016. H-FCN  
    project page: http://wanglimin.github.io/actionness\_hfcn/index.html  
    paper: http://wanglimin.github.io/papers/WangQTV\_CVPR16.pdf  
    github: https://github.com/wanglimin/actionness-estimation/

*  ### Real-time Action Recognition with Enhanced Motion Vector CNNs ###

intro: CVPR 2016  
    project page: http://zbwglory.github.io/MV-CNN/index.html  
    paper: http://wanglimin.github.io/papers/ZhangWWQW\_CVPR16.pdf  
    github: https://github.com/zbwglory/MV-release

*  ### Temporal Segment Networks: Towards Good Practices for Deep Action Recognition ###

intro: ECCV 2016. HMDB51: 69.4%, UCF101: 94.2%  
    arxiv: http://arxiv.org/abs/1608.00859  
    paper: http://wanglimin.github.io/papers/WangXWQLTV\_ECCV16.pdf  
    github: https://github.com/yjxiong/temporal-segment-networks

*  ### Temporal Segment Networks for Action Recognition in Videos ###

intro: An extension of submission http://arxiv.org/abs/1608.00859  
    arxiv: https://arxiv.org/abs/1705.02953

*  ### Hierarchical Attention Network for Action Recognition in Videos ###

arxiv: http://arxiv.org/abs/1607.06416

*  ### DeepCAMP: Deep Convolutional Action & Attribute Mid-Level Patterns ###

intro: CVPR 2016  
    arxiv: http://arxiv.org/abs/1608.03217

*  ### Depth2Action: Exploring Embedded Depth for Large-Scale Action Recognition ###

arxiv: http://arxiv.org/abs/1608.04339

*  ### Dynamic Image Networks for Action Recognition ###

intro: CVPR 2016  
    arxiv: http://users.cecs.anu.edu.au/~sgould/papers/cvpr16-dynamic\_images.pdf  
    github: https://github.com/hbilen/dynamic-image-nets

*  ### Human Action Recognition without Human ###

arxiv: http://arxiv.org/abs/1608.07876

*  ### Temporal Convolutional Networks: A Unified Approach to Action Segmentation ###

arxiv: http://arxiv.org/abs/1608.08242  
    ECCV 2016 workshop: http://bravenewmotion.github.io/

*  ### Temporal Activity Detection in Untrimmed Videos with Recurrent Neural Networks ###

intro: Bachelor Thesis Report at ETSETB TelecomBCN  
    project page: https://imatge-upc.github.io/activitynet-2016-cvprw/  
    arxiv: http://arxiv.org/abs/1608.08128  
    github: https://github.com/imatge-upc/activitynet-2016-cvprw

*  ### Sequential Deep Trajectory Descriptor for Action Recognition with Three-stream CNN ###

arxiv: http://arxiv.org/abs/1609.03056

*  ### Semi-Coupled Two-Stream Fusion ConvNets for Action Recognition at Extremely Low Resolutions ###

arxiv: https://arxiv.org/abs/1610.03898

*  ### Spatiotemporal Residual Networks for Video Action Recognition ###

intro: NIPS 2016  
    arxiv: https://arxiv.org/abs/1611.02155

*  ### Action Recognition Based on Joint Trajectory Maps Using Convolutional Neural Networks ###

arxiv: https://arxiv.org/abs/1611.02447

*  ### Deep Recurrent Neural Network for Mobile Human Activity Recognition with High Throughput ###

arxiv: https://arxiv.org/abs/1611.03607

*  ### Joint Network based Attention for Action Recognition ###

arxiv: https://arxiv.org/abs/1611.05215

*  ### Temporal Convolutional Networks for Action Segmentation and Detection ###

arxiv: https://arxiv.org/abs/1611.05267

*  ### AdaScan: Adaptive Scan Pooling in Deep Convolutional Neural Networks for Human Action Recognition in Videos ###

arxiv: https://arxiv.org/abs/1611.08240

*  ### ActionFlowNet: Learning Motion Representation for Action Recognition ###

arxiv: https://arxiv.org/abs/1612.03052

*  ### Higher-order Pooling of CNN Features via Kernel Linearization for Action Recognition ###

intro: Australian Center for Robotic Vision & Data61/CSIRO  
    arxiv: https://arxiv.org/abs/1701.05432

*  ### Tube Convolutional Neural Network (T-CNN) for Action Detection in Videos ###

https://arxiv.org/abs/1703.10664

*  ### Temporal Action Detection with Structured Segment Networks ###

project page: http://yjxiong.me/others/ssn/  
    arxiv: https://arxiv.org/abs/1704.06228  
    github: https://github.com/yjxiong/action-detection

*  ### Recurrent Residual Learning for Action Recognition ###

https://arxiv.org/abs/1706.08807

*  ### Hierarchical Multi-scale Attention Networks for Action Recognition ###

https://arxiv.org/abs/1708.07590

*  ### Two-stream Flow-guided Convolutional Attention Networks for Action Recognition ###

intro: International Conference of Computer Vision Workshop (ICCVW), 2017  
    arxiv: https://arxiv.org/abs/1708.09268

*  ### Action Classification and Highlighting in Videos ###

https://arxiv.org/abs/1708.09522

*  ### Real-Time Action Detection in Video Surveillance using Sub-Action Descriptor with Multi-CNN ###

https://arxiv.org/abs/1710.03383

*  ### End-to-end Video-level Representation Learning for Action Recognition ###

keywords: Deep networks with Temporal Pyramid Pooling (DTPP)  
    arxiv: https://arxiv.org/abs/1711.04161

*  ### Fully-Coupled Two-Stream Spatiotemporal Networks for Extremely Low Resolution Action Recognition ###

intro: WACV 2018  
    arxiv: https://arxiv.org/abs/1801.03983

*  ### DiscrimNet: Semi-Supervised Action Recognition from Videos using Generative Adversarial Networks ###

https://arxiv.org/abs/1801.07230

*  ### A Fusion of Appearance based CNNs and Temporal evolution of Skeleton with LSTM for Daily Living Action Recognition ###

https://arxiv.org/abs/1802.00421

*  ### Real-Time End-to-End Action Detection with Two-Stream Networks ###

https://arxiv.org/abs/1802.08362

*  ### A Closer Look at Spatiotemporal Convolutions for Action Recognition ###

intro: CVPR 2018. Facebook Research  
    intro: R(2+1)D and Mixed-Convolutions for Action Recognition.  
    project page: https://dutran.github.io/R2Plus1D/  
    arxiv: https://arxiv.org/abs/1711.11248  
    github: https://github.com/facebookresearch/R2Plus1D

*  ### VideoCapsuleNet: A Simplified Network for Action Detection ###

https://arxiv.org/abs/1805.08162

*  ### Where and When to Look? Spatio-temporal Attention for Action Recognition in Videos ###

https://arxiv.org/abs/1810.04511  
Projects

*  ### A Torch Library for Action Recognition and Detection Using CNNs and LSTMs ###

intro: CS231n student project report  
    paper: http://cs231n.stanford.edu/reports2016/221\_Report.pdf  
    github: https://github.com/garythung/torch-lrcn

*  ### 2016 ActivityNet action recognition challenge. CNN + LSTM approach. Multi-threaded loading. ###

github: https://github.com/jrbtaylor/ActivityNet

*  ### LSTM for Human Activity Recognition ###

github: https://github.com/guillaume-chevalier/LSTM-Human-Activity-Recognition/  
    github(MXNet): https://github.com/Ldpe2G/DeepLearningForFun/tree/master/Mxnet-Scala/HumanActivityRecognition

*  ### Scanner: Efficient Video Analysis at Scale ###

intro: Locate and recognize faces in a video, Detect shots in a film, Search videos by image  
    github: https://github.com/scanner-research/scanner

*  ### Charades Starter Code for Activity Classification and Localization ###

intro: Activity Recognition Algorithms for the Charades Dataset  
    github: https://github.com/gsig/charades-algorithms

*  ### NonLocalNetwork and Sequeeze-Excitation Network ###

intro: MXNet implementation of Non-Local and Squeeze-Excitation network  
    github: https://github.com/WillSuen/NonLocalandSEnet

# 事件识别 #

*  ### TagBook: A Semantic Video Representation without Supervision for Event Detection ###

arxiv: http://arxiv.org/abs/1510.02899

*  ### AENet: Learning Deep Audio Features for Video Analysis ###

arxiv: https://arxiv.org/abs/1701.00599  
    github: https://github.com/znaoya/aenet

# 事件检测 #

*  ### DevNet: A Deep Event Network for Multimedia Event Detection and Evidence Recounting ###

paper: http://120.52.72.47/winsty.net/c3pr90ntcsf0/papers/devnet.pdf  
    paper: http://www.cv-foundation.org/openaccess/content\_cvpr\_2015/papers/Gan\_DevNet\_A\_Deep\_2015\_CVPR\_paper.pdf

*  ### Detecting events and key actors in multi-person videos ###

intro: CVPR 2016  
    arxiv: http://arxiv.org/abs/1511.02917  
    paper: www.cv-foundation.org/openaccess/content\_cvpr\_2016/papers/Ramanathan\_Detecting\_Events\_and\_CVPR\_2016\_paper.pdf  
    paper: http://vision.stanford.edu/pdf/johnson2016cvpr.pdf  
    blog: http://www.leiphone.com/news/201606/l1TKIRFLO3DUFNNu.html

*  ### Deep Convolutional Neural Networks and Data Augmentation for Acoustic Event Detection ###

intro: INTERSPEECH 2016  
    arxiv: https://arxiv.org/abs/1604.07160

*  ### Efficient Action Detection in Untrimmed Videos via Multi-Task Learning ###

arxiv: https://arxiv.org/abs/1612.07403

*  ### Joint Event Detection and Description in Continuous Video Streams ###

intro: Joint Event Detection and Description Network (JEDDi-Net)  
    arxiv: https://arxiv.org/abs/1802.10250

# #