人工智能/数据科学比赛汇总 2019.7

ゝ一世哀愁。 2021-12-13 02:27 5428阅读 0赞

## Data Science for Good: [CareerVillage.org][] ##

[https://www.kaggle.com/c/data-science-for-good-careervillage][https_www.kaggle.com_c_data-science-for-good-careervillage]

Feb 26th - April 30th, 2019 // Host by [Kaggle][] // Prize: $15,000

**Note**: Match career advice questions with professionals in the field

Entry Deadline:

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## AI Drug Discovery: Pharmacokinetic Parameter Prediction ##

[https://signate.jp/competitions/168][https_signate.jp_competitions_168]

6月27日 - 11月2019年 // Host by [SIGNATE][] // Prize: ¥2,000,000

**Note**: Predict pharmacokinetic parameters from compound information and experimental data in the screening test.

Entry Deadline:

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## CCKS 2019 医疗命名实体识别 ##

[https://www.biendata.com/competition/ccks\_2019\_1/][https_www.biendata.com_competition_ccks_2019_1]

04/01 - 08/15 2019 // Host by [Biendata][] // Prize: ￥15,000

**Note**: 本任务是CCKS围绕中文电子病历语义化开展的系列评测的一个延续，在CCKS 2017，2018医疗命名实体识别评测任务的基础上进行了延伸和拓展。  
包括两个子任务：1）医疗命名实体识别：由于国内没有公开可获得的面向中文电子病历医疗实体识别数据集，本年度保留了医疗命名实体识别任务，对2018年度数据集做了修订，并随任务一同发布。2）医疗实体及属性抽取（跨院迁移）：在医疗实体识别的基础上，对预定义实体属性进行抽取。

Entry Deadline:

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## CCKS 2019 中文短文本的实体链指 ##

[https://www.biendata.com/competition/ccks\_2019\_el/][https_www.biendata.com_competition_ccks_2019_el]

04/20 - 07/30 2019 // Host by [Biendata][] // Prize: ￥10,000

**Note**: 输入文件包括若干行中文短文本。输出文本每一行包括此中文短文本的实体识别与链指结果，需识别出文本中所有mention（包括实体与概念），每个mention包含信息如下：mention在给定知识库中的ID，mention名和在中文短文本中的位置偏移。

Entry Deadline:

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## KDD Cup 2019 ##

[https://www.4paradigm.com/competition/kddcup2019][https_www.4paradigm.com_competition_kddcup2019]

Apr 1st - Jul 16th, 2019 // Host by [4Paradigm][] & [ChaLearn][] & [CodaLab][] // Prize: $65,000

**Note**: The 5th AutoML Challenge: AutoML for Temporal Relational Data  
In this challenge, participants are invited to develop AutoML solutions to binary classification problems for temporal relational data.

Entry Deadline:

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## SMP - ECISA “拓尔思杯”中文隐式情感分析评测 2019 ##

[https://biendata.com/competition/smpecisa2019/][https_biendata.com_competition_smpecisa2019]

04/24 - 08/18 2019 // Host by [Biendata][] // Prize: 30000元

**Note**: 欢迎来到SMP2019“拓尔思杯”中文隐式情感分析评测（The Evaluation of Chinese Implicit Sentiment Analysis，SMP-ECISA 2019）。  
我们将隐式情感定义为：“不含有显式情感词，但表达了主观情感的语言片段”，并将其划分为事实型隐式情感和修辞型隐式情感。其中，修辞型隐式情感又可细分为隐喻/比喻型、反问型以及反讽型。本次评测任务中，仅针对隐式情感的识别与情感倾向性分类。

Entry Deadline:

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## 7th Emotion Recognition in the Wild Challenge (EmotiW) ##

[https://sites.google.com/view/emotiw2019][https_sites.google.com_view_emotiw2019]

February - July, 2019 // Host by [ICMI 2019][] // Prize: NaN

**Note**: The goal of this challenge is to extend and carry forward the new common platform for evaluation of emotion recognition methods in real-world conditions defined in EmotiW2018 Grand Challenge held at the ACM International Conference on Multimodal Interaction 2018. This year there will be three sub-challenges:  
Audio-video based emotion recognition sub-challenge (AV)  
Group-level Cohesion sub-challenge (GC)  
Engagement prediction in the Wild (EW)

Entry Deadline:

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## Shared Task on Hierarchical Classification of Blurbs ##

[https://competitions.codalab.org/competitions/21226][https_competitions.codalab.org_competitions_21226]

Feb. 1 - Aug, 2019 // Host by [CodaLab][CodaLab 1] & [KONVENS 2019][] // Prize: NaN

**Note**: This task is focusing on classifying **German** books into their respective hierarchically structured writing genres using short advertisement texts (Blurbs) and further meta information such as author, page number, release date, etc.

Entry Deadline:

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## eBay SIGIR 2019 eCommerce Search Challenge: High Accuracy Recall Task ##

[https://sigir-ecom.github.io/data-task.html][https_sigir-ecom.github.io_data-task.html]

May 17 - July 25, 2019 // Host by [EvalAI][] & [The 2019 SIGIR Workshop On eCommerce][] // Prize: NaN

**Note**: The challenge data consists of a set of popular search queries and a fair size set of candidate documents. Challenge participants make a boolean relevant-or-not decision for each query-document pair. Human judgments are used to create labeled training and evaluation data for a subset of the query-document pairs. Evaluation of submissions will be based on the traditional F1 metric, incorporating components of both recall and precision.

Entry Deadline:

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## 2019 年县域农业大脑AI挑战赛 ##

[https://tianchi.aliyun.com/competition/entrance/231717/introduction][https_tianchi.aliyun.com_competition_entrance_231717_introduction]

6月05 - 9月25, 2019 // Host by [天池][Link 5] // Prize: ￥300000

**Note**: 本次大赛，我们选择了具有独特的地理环境、气候条件以及人文特色的贵州省兴仁市作为研究区域，聚焦当地的特色优势产业和支柱产业——薏仁米产业， 以薏仁米作物识别以及产量预测为比赛命题，要求选手开发算法模型，通过无人机航拍的地面影像，探索作物分类的精准算法，识别薏仁米、玉米、烤烟三大作物类型，提升作物识别的准确度，降低对人工实地勘察的依赖，提升农业资产盘点效率，并结合产量标注数据预测当年的薏仁米产量，提升农业精准管理能力。

Entry Deadline:

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## 第五届中间件性能挑战赛 ##

[https://tianchi.aliyun.com/competition/entrance/231714/][https_tianchi.aliyun.com_competition_entrance_231714]

6月10 - 7月22, 2019 // Host by [天池][Link 5] // Prize: $200,000

**Note**:  
初赛：《自适应负载均衡的设计实现》  
复赛：《实现一个进程内基于队列的消息持久化存储引擎》

Entry Deadline:

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## CCKS 2019 中文知识图谱问答 ##

[https://www.biendata.com/competition/ccks\_2019\_6/][https_www.biendata.com_competition_ccks_2019_6]

04/20 - 07/30 2019 // Host by [Biendata][] // Prize: ￥15,000

**Note**: 本评测任务为基于中文知识图谱的自然语言问答，简称CKBQA （Chinese Knowledge Base Question Answering）。即输入一句中文问题，问答系统从给定知识库中选择若干实体或属性值作为该问题的答案。问题均为客观事实型，不包含主观因素。理解并回答问题的过程中可能需要进行实体识别、关系抽取、语义解析等子任务。这些子任务的训练可以使用额外的资源，但是最终的答案必须来自给定的知识库。

Entry Deadline:

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## MLCAS 2019 - Sorghum head detection ##

[https://www.register.extension.iastate.edu/mlcas2019/competition][https_www.register.extension.iastate.edu_mlcas2019_competition]

June 10 - July 21, 2019 // Host by [CodaLab][CodaLab 2] & [MLCAS 2019][] // Prize: $37,000

**Note**: This challenge uses RGB image frames of Sorghum heads collected from an UAV. The dataset consists of 1300 images wth sorghum head annotations. You can find further information about the datasets for Training phase and Final test phase in the “Participate” tab under the heading “Get Data”. The datasets could be downloaded from the “Participate” tab under the heading “Files”. The training dataset contains both labelled images and unlabelled images. Participants are encouraged to use algorithms from active learning, semi-supervised learning or unsupervised learning for utilizing the unlabelled images during training.

Entry Deadline:

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## Recursion Cellular Image Classification ##

[https://www.kaggle.com/c/recursion-cellular-image-classification][https_www.kaggle.com_c_recursion-cellular-image-classification]

Now - Sept 26, 2019 // Host by [Kaggle][] & [NeurIPS 2019][] // Prize: $13,000

**Note**: CellSignal: Disentangling biological signal from experimental noise in cellular images

Entry Deadline:

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## 全球数据智能大赛(2019)——“数字人体”赛场一：肺部CT多病种智能诊断 ##

[https://tianchi.aliyun.com/competition/entrance/231724/][https_tianchi.aliyun.com_competition_entrance_231724]

6月24 - 9月09, 2019 // Host by [天池][Link 5] // Prize: $900,000

**Note**: 赛场一“数字人体”挑战赛以肺部CT多病种智能诊断为课题，开放高质量CT标注数据，要求选手提出并综合运用目标检测、深度学习等人工智能算法，识别肺结节、索条（条索状影）、动脉硬化或钙化、淋巴结钙化等多个病种，避免同一部位单病种的反复筛查，提高检测的速度和精度，辅助医生进行诊断。

Entry Deadline:

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## Online Challenge: Build A Recommendation Engine (Powered by IBM Cloud) ##

[https://datahack.analyticsvidhya.com/contest/build-a-recommendation-engine-powered-by-ibm-cloud/][https_datahack.analyticsvidhya.com_contest_build-a-recommendation-engine-powered-by-ibm-cloud]

24 Jan - 25 July, 2019 // Host by [Analytics Vidhya][] // Prize: INR 50,000

**Note**: You are expected to build a high performing recommendation engine using any framework of your choice. You are encouraged to use IBM Watson Studio Apache spark based Jupyter notebook.

Entry Deadline:

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## WIDER Face & Person Challenge 2019 ##

[http://wider-challenge.org/2019.html][http_wider-challenge.org_2019.html]

May 8 - July 25, 2019 // Host by [CodaLab][CodaLab 1] & [ICCV 2019][] & [商汤][Link 6] & [Amazon][] // Prize: cash prize and AWS credits

**Note**: Following the success of the [First WIDER Challenge Workshop][], we organize a new round of challenge in conjunction with [ICCV 2019][]. The challenge centers around the problem of precise localization of human faces and bodies, and accurate association of identities. It comprises of four tracks:  
[WIDER Face Detection][], aims at soliciting new approaches to advance the state-of- the-art in face detection.  
[WIDER Pedestrian Detection][], has the goal of gathering effective and efficient approaches to address the problem of pedestrian detection in unconstrained environments.  
[WIDER Cast Search by Portrait][], presents an exciting challenge of searching cast across hundreds of movies.  
[WIDER Person Search by Language][], aims to seek new approaches to search person by natural language.

Entry Deadline:

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## "华为云杯"2019深圳开放数据应用创新大赛 ##

[https://opendata.sz.gov.cn/sodic2019/][https_opendata.sz.gov.cn_sodic2019]

2019-06-19 至 2019-09-07 // Host by [深圳市政府数据开放平台][Link 7] & [华为 HUAWEI][HUAWEI]// Prize: 1400000元 + 300000元华为云资源

**Note**: 赛题数据：  
交通数据 室内停车 公租房轮候 卫星遥感 文体公益活动 游客预约 道路积水 深圳图书馆进馆人次统计 龙岗区坂田街道交通流量 企业信用目录 坪山区民生诉求数据 坪山区河流域和易积水道路视频 光明区政府服务办事大厅预约

Entry Deadline:

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## 第四届魔镜杯大赛 ##

[https://ai.ppdai.com/mirror/goToMirrorDetail?mirrorId=17][https_ai.ppdai.com_mirror_goToMirrorDetail_mirrorId_17]

2019-05-28 至 2019-07-27 // Host by [拍拍贷·AI][AI] & [DC][] // Prize: ￥370000

**Note**: 本次比赛以互联网金融信贷业务为背景，参赛选手需要利用提供的数据，预测资产组合在未来一段时间内每日的回款金额。赛题涵盖了信贷违约预测、现金流预测等金融领域常见问题，同时又是复杂的时序问题和多目标预测问题。

Entry Deadline:

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## CCKS 2019 面向金融领域的事件主体抽取 ##

[https://www.biendata.com/competition/ccks\_2019\_4/][https_www.biendata.com_competition_ccks_2019_4]

05/01 - 07/30 2019 // Host by [Biendata][] // Prize: ￥15,000

**Note**: 本次评测任务的主要目标是从真实的新闻语料中，抽取特定事件类型的主体。即给定一段文本T，和文本所属的事件类型S，从文本T中抽取指定事件类型S的事件主体。

Entry Deadline:

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## 全国高校大数据应用创新大赛 ##

[https://ai.futurelab.tv/contest\_detail/4][https_ai.futurelab.tv_contest_detail_4]

6月8日 - 9月, 2019 // Host by [睡前FUTURE.AI][FUTURE.AI] // Prize: 20,000元 x 2

**Note**: 全国高校大数据应用创新大赛”（以下简称大赛）是由教育部高等学校计算机类专业教学指导委员会、中国工程院中国工程科技知识中心和联合国教科文组织国际工程科技知识中心联合主办，复旦大学计算机学院承办，面向全国高校在校学生的，年度性大数据学科竞赛。 通用赛道：  
[大数据技术技能赛][Link 8]: 大赛提供的数据和自选数据建立并训练模型，使之能够预测给定地区、日期和前置气象条件下，未来7天的部分气象要素的变化情况;  
[大数据与人工智能创意赛][Link 9]: 本次大赛气象大数据开放式命题赛道，提供过去5年若干城市的气象数据，参赛选手可自主运用和扩充数据，设计一个基于气象大数据的跨行业跨领域的应用解决方案。

Entry Deadline:

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## DeepFashion2 Challenge 2019 ##

[https://sites.google.com/view/cvcreative/deepfashion2?authuser=0][https_sites.google.com_view_cvcreative_deepfashion2_authuser_0]

May 27 - July 30, 2019 // Host by [CodaLab][CodaLab 3] // Prize: NaN

**Note**: DeepFashion2 ([github][]) is a comprehensive fashion dataset. It contains 491K diverse images of 13 popular clothing categories from both commercial shopping stores and consumers. It totally has 801K clothing clothing items, where each item in an image is labeled with scale, occlusion, zoom-in, viewpoint, category, style, bounding box, dense landmarks and per-pixel mask.There are also 873K Commercial-Consumer clothes pairs.  
The dataset is split into a training set (391K images), a validation set (34k images), and a test set (67k images).  
[Track 1 Clothes Landmark Estimation][]  
[Track 2 Clothes Retrieval][]

Entry Deadline:

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## 莱斯杯：全国第二届“军事智能机器阅读"挑战赛" ##

[https://www.kesci.com/home/competition/5d142d8cbb14e6002c04e14a][https_www.kesci.com_home_competition_5d142d8cbb14e6002c04e14a]

2019-09-03 至 2019-10-28 // Host by [Kesci][] // Prize: 50万元人民币

**Note**: 本次竞赛提供的大规模中文阅读理解数据集，共包含15万余篇的专业文章，7万个军事类复杂问题，每个问题对应五篇文章

Entry Deadline:

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## 第三届"长风杯"大数据分析与挖掘竞赛 ##

[http://contest.cfdsj.cn/index/care][http_contest.cfdsj.cn_index_care]

2019-05-15 至 2019-10-31 // Host by [长风大数据平台][Link 10] // Prize: ￥5万

**Note**: 第三届“长风杯”大数据分析与挖掘竞赛是一场面向全国普通高等院校经济与管理类、信息技术类等专业在校大学生的全国性赛事。  
长风大数据平台将向本次竞赛的参赛者免费开放物流、电商、交通、公共、贸易等多行业的海量数据资源；其他生产型/服务型企业所提供真实数据。

Entry Deadline:

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## “添翼杯”人工智能创新应用大赛 ##

[https://tianyicup.kesci.com/][https_tianyicup.kesci.com]

2019-06-14 至 2019-09-20 // Host by [上海电信][https_tianyicup.kesci.com] // Prize: 40,000 元 x 2

**Note**:  
智慧环保-垃圾分类图像检测问题: 请参赛选手利用训练集图片，建立算法模型，对测试集给定的物品图片，判断其属于可回收垃圾的概率。  
智慧教育-成绩预测问题：请参赛选手利用脱敏后的初中学生过往考试情况与考试考点信息，建立算法模型，预测学生初中最后一次期末考试的成绩。

Entry Deadline:

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## Northeastern SMILE Lab - Recognizing Faces in the Wild ##

[https://www.kaggle.com/c/recognizing-faces-in-the-wild/overview/description][https_www.kaggle.com_c_recognizing-faces-in-the-wild_overview_description]

Now - August 8, 2019 // Host by [Kaggle][] // Prize: NaN

**Note**: Can you determine if two individuals are related?

Entry Deadline:

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## 首届中文NL2SQL挑战赛 ##

[https://tianchi.aliyun.com/competition/entrance/231716/introduction][https_tianchi.aliyun.com_competition_entrance_231716_introduction]

6月24 - 9月, 2019 // Host by [天池][Link 5] // Prize: ￥十五万

**Note**: 首届中文NL2SQL挑战赛，使用金融以及通用领域的表格数据作为数据源，提供在此基础上标注的自然语言与SQL语句的匹配对，希望选手可以利用数据训练出可以准确转换自然语言到SQL的模型。

Entry Deadline:

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## Segmentation of THoracic Organs at Risk in CT images (SegTHOR) ##

[https://competitions.codalab.org/competitions/21012][https_competitions.codalab.org_competitions_21012]

Jan. 5 - Aug 8, 2019 // Host by [CodaLab][CodaLab 1] & [ISBI 2019][] // Prize: NaN

**Note**: The goal of the SegTHOR challenge is to automatically segment 4 OAR: heart, aorta, trachea, esophagus. Participants will be provided with a training set 40 CT scans with manual segmentation. The test set will include 20 CT scans.

Entry Deadline:

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## 安泰杯 —— 跨境电商智能算法大赛 ##

[https://tianchi.aliyun.com/competition/entrance/231718/introduction?spm=5176.12281949.1003.1.3a994c2axCiMpB][https_tianchi.aliyun.com_competition_entrance_231718_introduction_spm_5176.12281949.1003.1.3a994c2axCiMpB]

7月16 - 9月16, 2019 // Host by [天池][Link 5] // Prize: ￥100000

**Note**: 本次比赛给出若干日内来自成熟国家的部分用户的行为数据，以及来自待成熟国家的A部分用户的行为数据，以及待成熟国家的B部分用户的行为数据去除每个用户的最后一条购买数据，让参赛人预测B部分用户的最后一条行为数据。

Entry Deadline:

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## Generative Dog Images ##

[https://www.kaggle.com/c/generative-dog-images][https_www.kaggle.com_c_generative-dog-images]

Now - August 9, 2019 // Host by [Kaggle][] // Prize: $10,000

**Note**: Experiment with creating puppy pics

Entry Deadline:

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## Challenges and Opportunities in Automated Coding of COntentious Political Events (Cope 2019) @Euro CSS 2019 ##

[https://emw.ku.edu.tr/?event=challenges-and-opportunities-in-automated-coding-of-contentious-political-events][https_emw.ku.edu.tr_event_challenges-and-opportunities-in-automated-coding-of-contentious-political-events]

6/7 - 9/2, 2019 // Host by [CodaLab][CodaLab 4] & [Euro CSS 2019][] // Prize: NaN

**Note**: We use English online news archives from India and China as data sources to create the training and test corpora. India and China are the source and the target countries respectively in our setting.

Entry Deadline:

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## 遥感图像稀疏表征与智能分析竞赛 ##

[http://rscup.bjxintong.com.cn/][http_rscup.bjxintong.com.cn]

2019-06-01 至 2019-09-20 // Host by [中国科学院空间应用工程与技术中心][Link 11] // Prize: ￥160000

**Note**: 本次大赛设置[遥感图像场景分类][Link 12]、 [遥感图像目标检测][Link 13]、 [遥感图像语义分割][Link 14]、 [遥感图像变化检测][Link 15]和 [遥感卫星视频目标跟踪][Link 16]五个竞赛单元，并在决赛中设置 [基于华为昇腾AI处理器的遥感图像解译加分赛][AI 1]。 组织方将提供面向各竞赛单元的大规模遥感图像精确标注数据集与标准规范的测试数据， 制定可量化的算法评测标准，通过初赛、决赛和复审答辩等多个阶段的评比， 遴选出优秀的遥感图像解译算法，决胜出优胜团队。

Entry Deadline:

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## CIKM 2019 EComm AI ##

[https://tianchi.aliyun.com/competition/entrance/231719/][https_tianchi.aliyun.com_competition_entrance_231719]

7月05 - 9月25, 2019 // Host by [天池][Link 5] // Prize: $25000 + $25000

**Note**:  
[Predicting User Behavior Diversities in A Dynamic Interactive Environment][]  
[Efficient and Novel Item Retrieval for Large-scale Online Shopping Recommendation][]

Entry Deadline:

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## ARIEL Data Challenge Series 2019 ##

[https://ariel-datachallenge.azurewebsites.net/][https_ariel-datachallenge.azurewebsites.net]

15th of August 2019 // Host by [ECML-PKDD 2019][] // Prize: Eternal gratitude … or a bottle of wine.

**Note**: ARIEL, a mission to make the first large-scale survey of exoplanet atmospheres, has launched a global competition series to find innovative solutions for the interpretation and analysis of exoplanet data. You can find our press release [here][].  
[The first ARIEL Data Challenge][] invites professional and amateur data scientists around the world to use Machine Learning (ML) to remove noise from exoplanet observations caused by starspots and by instrumentation.  
[A second ARIEL Data Challenge][] that focuses on the retrieval of spectra from simulations of cloudy and cloud-free super-Earth and hot-Jupiter data was also launched today.  
[A further data analysis challenge][] to create pipelines for faster, more effective processing of the raw data gathered by the mission will be launched in June.

Entry Deadline:

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

## 2nd 3D Face Alignment in the Wild Challenge - Dense Reconstruction from Video ##

[https://competitions.codalab.org/competitions/23626][https_competitions.codalab.org_competitions_23626]

July 4 - Aug 15 2019 // Host by [CodaLab][https_competitions.codalab.org_competitions_23626] // Prize: NaN

**Note**: The 2nd 3DFAW Challenge evaluates 3D face reconstruction methods on a new large corpora of profile-to-profile face videos annotated with corresponding high-resolution 3D ground truth meshes. The corpora includes profile-to-profile videos obtained under a range of conditions:  
high-definition in-the-lab video,  
unconstrained video from an iPhone device

Entry Deadline:

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

## 飯田産業 土地の販売価格の推定 ##

[https://signate.jp/competitions/162][https_signate.jp_competitions_162]

6月10日 - 8月2019年 // Host by [SIGNATE][] // Prize: ¥2,300,000

**Note**: 「日本語をネイティブに話せる方」

Entry Deadline:

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

## AI开发者大赛 ##

[https://www.dcjingsai.com/][https_www.dcjingsai.com]

5月21日-9月21日, 2019 // Host by [DC 竞赛][DC 1] & [科大讯飞][Link 17] // Prize: 1000000 x 8

**Note**:  
[AI开发者大赛-工程机械核心部件寿命预测挑战赛][AI_-]  
[AI开发者大赛-大数据应用分类标注挑战赛][AI_- 1]  
[AI开发者大赛-广告营销反作弊算法挑战赛][AI_- 2]  
[AI开发者大赛-阿尔茨海默综合征预测挑战赛][AI_- 3]

Entry Deadline:

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

## AutoCV2: Image and video Classification ##

[https://autodl.lri.fr/competitions/146][https_autodl.lri.fr_competitions_146]

July 2 - Aug 20, 2019 // Host by [AutoDL][] & [NeurIPS 2019][NeurIPS 2019 1] // Prize: 4000 USD

**Note**: This is round 2 of AutoCV: Image + Video! This is a 2-phase challenge, see the challenge rules for details. This is the FEED-BACK PHASE. The second phase (final blind-test phase) will be run from a separate submission site, to be announced after the end of the feed-back phase.

Entry Deadline:

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

## iFLYTEK AI 开发者大赛 ##

[http://challenge.xfyun.cn/2019/][http_challenge.xfyun.cn_2019]

5月21日 - 10月14日, 2019 // Host by [讯飞开放平台][Link 17] // Prize: 100万 RMB

**Note**: "iFLYTEK AI 开发者大赛"是由科大讯飞发起的顶尖人工智能竞赛平台，汇聚产学研各界力量，面向全球开发者发起数据算法及创新应用类挑战，推动人工智能前沿科学研究和创新成果转化，培育人工智能产业人才，助力人工智能生态建设。 2019 年，第二届 iFLYTEK AI 开发者大赛将继续开放科大讯飞优质大数据资源及人工智能核心技术，面向全球开发者发起数据算法及创新应用类挑战。  
**阿尔茨海默综合症预测挑战赛**: 基于老年人在特定图片描述任务中产生的语音，给定语音数据中提取出的声学特征、主被试对话的切分信息、人工文本转写结果以及对应的认知标签，建立2分类模型预测认知标签（正常或认知障碍）。  
**移动广告反欺诈算法挑战赛**: 移动广告反欺诈需要强大的数据作为支撑，本次大赛提供了讯飞AI营销云海量的现网流量数据作为训练样本，参赛选手需基于提供的样本构建模型，预测流量作弊与否。  
**大数据应用分类标注挑战赛**: 选手基于提供的应用二级分类标签以及若干随机应用标注样本，实现应用分类标注算法（每个应用一个标签，以应用最主要属性对应的标签为该应用的标签）。  
**工程机械核心部件寿命预测挑战赛**: 由中科云谷科技有限公司提供某类工程机械设备的核心耗损性部件的工作数据，包括部件工作时长、转速、温度、电压、电流等多类工况数据。希望参赛者利用大数据分析、机器学习、深度学习等方法，提取合适的特征、建立合适的寿命预测模型，预测核心耗损性部件的剩余寿命。

Entry Deadline:

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

## SIIM-ACR Pneumothorax Segmentation ##

[https://www.kaggle.com/c/siim-acr-pneumothorax-segmentation][https_www.kaggle.com_c_siim-acr-pneumothorax-segmentation]

Now - Sept 22, 2019 // Host by [Kaggle][] & [C-MIMI 2019][] // Prize: $30,000

**Note**: Identify Pneumothorax disease in chest x-rays

Entry Deadline:

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

## Predicting Molecular Properties ##

[https://www.kaggle.com/c/champs-scalar-coupling][https_www.kaggle.com_c_champs-scalar-coupling]

Now - August 28, 2019 // Host by [Kaggle][] // Prize: $30,000

**Note**: Can you measure the magnetic interactions between a pair of atoms?

Entry Deadline:

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

## APTOS 2019 Blindness Detection ##

[https://www.kaggle.com/c/aptos2019-blindness-detection][https_www.kaggle.com_c_aptos2019-blindness-detection]

Now - Sept 5, 2019 // Host by [Kaggle][] & [4th APTOS Symposium][] // Prize: $50,000

**Note**: Detect diabetic retinopathy to stop blindness before it’s too late

Entry Deadline:

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

## QMUL Surveillance Face Recognition Challenge @ ICCV2019 workshop RLQ ##

[https://qmul-survface.github.io/][https_qmul-survface.github.io]

27 June - 30 Aug, 2019 // Host by [EvalAI][EvalAI 1] & [ICCV 2019][] // Prize: NaN

Entry Deadline:

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

## “达观杯”文本智能信息抽取挑战赛 ##

[https://www.biendata.com/competition/datagrand/][https_www.biendata.com_competition_datagrand]

06/28 - 08/31 2019 // Host by [Biendata][] // Prize: 七万七千元

**Note**: 本次大赛的任务是给定一定数量的标注语料以及海量的未标注语料，在3个字段上做信息抽取任务。

Entry Deadline:

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

## Challenge on Deep Learning based Loop Filter for Video Coding ##

[http://challenge.ai.iqiyi.com/detail?raceId=5b112a742a360316a898ff50][http_challenge.ai.iqiyi.com_detail_raceId_5b112a742a360316a898ff50]

May, 25th - May, 31st, 2018 // Host by [爱奇艺|iQIYI][iQIYI] & [AVS Workgroup][] // Prize: NaN

**Note**: The participants are encouraged to investigate neural network based methods (especially convolutional neural networks) with different network structures, in a hope of achieving the best quality with lightest network configuration for a good tradeoff of efficiency and complexity.

Entry Deadline:

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

## CoNLL 2019 Shard Task on Cross-Framework Meaning Representation Parsing ##

[http://mrp.nlpl.eu/][http_mrp.nlpl.eu]

March 6 - November 3, 2019 // Host by [CodaLab][CodaLab 5] // Prize: NaN

**Note**: The 2019 Conference on Computational Language Learning (CoNLL) hosts a shared task (or ‘system bake-off’) on Cross-Framework Meaning Representation Parsing (MRP 2019).  
The goal of the task is to advance data-driven parsing into graph-structured representations of sentence meaning.

Entry Deadline:

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

## The 2nd Large-scale Video Object Segmentation Challenge ##

[https://youtube-vos.org/challenge/2019/][https_youtube-vos.org_challenge_2019]

May. 20 - Sep. 5 2019 // Host by [CodaLab][CodaLab 1] & [ICCV 2019][] // Prize: NaN

**Note**: As a continuous effort to push forward the research on video object segmentation tasks, we plan to host a second workshop with a challenge based on the YouTube-VOS dataset, targeting at more diversified problem settings, i.e., we plan to provide two challenge tracks in this workshop.  
[Track 1: Video Object Segmentation][Track 1_ Video Object Segmentation]  
[Track 2: Video Instance Segmentation][Track 2_ Video Instance Segmentation]

Entry Deadline:

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

## The 3rd YouTube-8M Video Understanding Challenge ##

[https://www.kaggle.com/c/youtube8m-2019][https_www.kaggle.com_c_youtube8m-2019]

Now - October 28, 2019 // Host by [Kaggle][] & [ICCV 2019][] // Prize: $25,000

**Note**: Temporal localization of topics within video

Entry Deadline:

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

## Automatic Structure Segmentation for Radiotherapy Planning Challenge 2019 ##

[https://structseg2019.grand-challenge.org/][https_structseg2019.grand-challenge.org]

June 15 - Oct 1, 2019 // Host by [Grand Challenges][https_structseg2019.grand-challenge.org] & [MICCAI 2019][] // Prize: NaN

**Note**: The goal of the challenge is to set up tasks for evaluating automatic algorithms on segmentation of organs-at-risk (OAR) and gross target volume (GTV) of tumors of two types of cancers, nasopharynx cancer and lung cancer, for radiation therapy planning. There are four tasks for evaluating the performance of the algorithms. Participants can choose to join all or either tasks according to their interests.  
Task 1: Organ-at-risk segmentation from head & neck CT scans.  
Task 2: Gross Target Volume segmentation of nasopharynx cancer.  
Task 3: Organ-at-risk segmentation from chest CT scans.  
Task 4: Gross Target Volume segmentation of lung cancer.

Entry Deadline:

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

## OpenEDS Challenge ##

[https://research.fb.com/programs/openeds-challenge][https_research.fb.com_programs_openeds-challenge]

May 3 - Sep 16, 2019 // Host by [EvalAI][EvalAI 2] & [Facebook][] // Prize: $13,000 USD x2

**Note**: In the absence of accurate gaze labels, we propose to advance the state of the art by carefully designing two challenges that combine human annotation of eye features with unlabeled data. These challenges focus on deeper understanding of the distribution underlying human eye state. We invite ML and CV researchers for participation.  
[Track-1 Semantic Segmentation challenge][]  
[Track-2 Synthetic Eye Generation challenge][]

Entry Deadline:

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

## Exoplanet imaging data challenge ##

[https://exoplanet-imaging-challenge.github.io/][https_exoplanet-imaging-challenge.github.io]

May 16th - Sep 16th, 2019 // Host by [CodaLab][CodaLab 6] // Prize: NaN

**Note**: This competition is composed of two sub-challenges focusing on the two most widely used observing techniques: **pupil tracking** (angular differential imaging, ADI) and **multi-spectral imaging combined with pupil tracking** (multi-channel spectral differential imaging, ADI+mSDI).

Entry Deadline:

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

## 成语阅读理解大赛 ##

[https://www.biendata.com/competition/idiom/][https_www.biendata.com_competition_idiom]

06/25 - 09/25 2019 // Host by [Biendata][] // Prize: ￥24,000元

**Note**: 本次竞赛将基于选词填空的任务形式，提供大规模的成语填空训练语料。在给定若干段文本下，选手需要在提供的候选项中，依次选出填入文本中的空格处最恰当的成语。

Entry Deadline:

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

## Peking University International Competition on Ocular Disease Intelligent Recognition (ODIR-2019) ##

[https://odir2019.grand-challenge.org/][https_odir2019.grand-challenge.org]

May 18 - Sep 25, 2019 // Host by [Grand Challenges][] & [北京大学][Link 18] // Prize: 10,00,000 RMB (140,000+ USD)

**Note**: **北京大学’智慧之眼’国际眼科疾病智能识别竞赛**  
The SG will provide participants with 5,000 structured desensitized ophthalmologic image set of patient’s age, sex, binocular color fundus photos and doctors’ diagnostic report.  
上工医信将为参赛者提供5000组包含患者的性别、年龄、双眼彩色眼底照片和医生印象报告等的结构化脱敏后眼科的数据集。  
The purpose of this challenge is to compare approaches of ophthalmic disease classification in color fundus images. Participant will have to submit classification results of eight categories for all the testing data. For every category, a classification probability (value from 0.0 to 1.0) denotes risk of a patient diagnosed with corresponding category.  
该竞赛的目的是比较基于彩色眼底图像进行眼科疾病分类的不同方法。 参与者必须提交所有测试数据集的八个类别的分类结果。 对于每个类别，分类概率（值从0.0到1.0）表示患者被诊断为具有相应类别的可能性/风险。

Entry Deadline:

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

## Digestive-System Pathological Detection and Segmentation Challenge 2019 ##

[https://digestpath2019.grand-challenge.org/][https_digestpath2019.grand-challenge.org]

June 14 - Oct 1, 2019 // Host by [Grand Challenges][https_digestpath2019.grand-challenge.org] & [MICCAI 2019][] // Prize: NaN

**Note**: The goal of the challenge is to set up tasks for evaluating automatic algorithms on signet ring cell detection and colonoscopy tissue screening from digestive system pathological images. This will be the first challenge and first public dataset on signet ring cell detection and colonoscopy tissue screening. Releasing the large quantity of expert-level annotations on digestive-system pathological images will substantially advance the research on automatic pathological object detection and lesion segmentation.  
Task 1: Signet ring cell detection.  
Task 2: Colonoscopy tissue segmentation and classification.

Entry Deadline:

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

## The 2nd China (Hengqin) International University Quantitative Finance Competition ##

[http://qfc-c.com/][http_qfc-c.com]

2019-04-19 至 2020-03-21 // Host by [珠海市横琴新区金融服务中心][Link 19] // Prize: ￥140万

**Note**: 第二届中国（横琴）国际高校量化金融大赛  
**参赛要求** 参赛者应根据题目要求，完成一篇包括量化金融策略原理、模型的假设、建立和求解、计算方法的设计、分析和检验、模型的改进等方面的书面报告（即答卷）；并在规定竞赛期间内，将参赛策略的市场运行进行模拟仿真竞赛。根据参赛策略的测试结果（包括样本内和样本外）的收益水平及市场风险防范的效果等统一指标打分评比，以市场的标准来决定优劣，评价策略的回测和实盘模拟表现，同时考虑策略逻辑的稳健性和创新性。竞赛评奖以策略的合理性、建模的创新性、测试策略的市场适应性及收益风险水平等结果为主要标准。  
**Requirements** Participants should write a report covering quantitative financial strategy theories 1) Model theoretical hypothesis and description of quantitive model 2) Data analysis 3) Strategy back testing results and performance analysis. According to the requirements of the competition, participants’ strategies will be back tested and paper traded during the required period. Evaluation and scoring will base on unified measurements including return, volatility, max drawdown of the strategies and so on. The determination of merits and evaluation of strategy back test and paper trading performance will be made according to market standards, while the robustness and innovation of the strategic logic will also be taken into consideration. Key criteria will include the rationality of the strategy, the creativeness of the model, the market adaptability of the testing strategy and the level of return and risk.

Entry Deadline:

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

## Open Images 2019 ##

[https://storage.googleapis.com/openimages/web/challenge2019.html][https_storage.googleapis.com_openimages_web_challenge2019.html]

Now - Oct 27, 2019 // Host by [Kaggle][] & [ICCV 2019][] // Prize: $25,000

**Note**: This year’s [Open Images V5][] release enabled the second [Open Images Challenge][https_storage.googleapis.com_openimages_web_challenge2019.html] to include the following 3 tracks:  
[Object detection][] track for detecting bounding boxes around object instances, relaunched from 2018.  
[Visual relationship detection track][] for detecting pairs of objects in particular relations, also relaunched from 2018.  
Instance segmentation track \[Link to be provided when launched on July 1\], brand new for 2019.

Entry Deadline:

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

## Visual Domain Adaptation Challenge (VisDA-2019) ##

[http://ai.bu.edu/visda-2019/][http_ai.bu.edu_visda-2019]

April 9 - Sept. 27, 2019 // Host by [CodaLab][CodaLab 1] & [ICCV 2019][ICCV 2019 1] // Prize: NaN

**Note**: We are pleased to announce the 2019 Visual Domain Adaptation (VisDA2019) Challenge! It is well known that the success of machine learning methods on visual recognition tasks is highly dependent on access to large labeled datasets. Unfortunately, performance often drops significantly when the model is presented with data from a new deployment domain which it did not see in training, a problem known as dataset shift. The VisDA challenge aims to test domain adaptation methods’ ability to transfer source knowledge and adapt it to novel target domains.  
This challenge includes two tracks:  
[Multi-Source Domain Adaptation Challenge][]  
[Semi-Supervised Domain Adaptation][]

Entry Deadline:

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

## AI in RTC-超分辨率图像质量比较挑战赛 ##

[https://www.dcjingsai.com/common/cmpt/AI in RTC-超分辨率图像质量比较挑战赛\_竞赛信息.html][https_www.dcjingsai.com_common_cmpt_AI in RTC-_.html]

7月1日-10月23日, 2019 // Host by [DC 竞赛][DC 1] // Prize: 100000

**Note**: 单帧图像超分辨率近年来备受关注。同样的图像，在经过不同超分辨率算法处理后，获得的图像质量也有所不同。在这个挑战中，参赛者需要对100张图片进行4倍超分辨率处理。比赛最终以PI (perceptual index)指标作为评判标准，PI值越小，表明图像质量越高，得分越高，分值高的团队获得优胜。

Entry Deadline:

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

## AI in RTC-超分辨率算法性能比较挑战赛 ##

[https://www.dcjingsai.com/common/cmpt/AI in RTC-超分辨率算法性能比较挑战赛\_竞赛信息.html][https_www.dcjingsai.com_common_cmpt_AI in RTC-_.html 1]

7月1日-10月23日, 2019 // Host by [DC 竞赛][DC 1] // Prize: 100000

**Note**: 将超分辨算法用于处理实时视频流时，模型的处理表现与运算性能，是一个两难的选择。为了追求较低复杂度，可能需要牺牲图像质量；为了追求较高质量的输出，导致设备资源占用过高，产生设备发烫、视频模糊卡顿等现象。该挑战主要考察算法模型的性能，参赛者需要对图像做2倍的超分辨率处理，算法复杂度控制在1GFLOPs之内，我们以SRCNN模型为baseline, 并采用PSNR、SSIM及运行时间来综合评估算法的性能，分值高者即获胜。

Entry Deadline:

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

## Alchemy Contest ##

[https://alchemy.tencent.com/][https_alchemy.tencent.com]

5/22 - 9/30, 2019 // Host by [CodaLab][CodaLab 7] & [Tencent Quantum Lab 腾讯量子实验室][Tencent Quantum Lab]// Prize: total ￥100,000 RMB

**Note**: The Tencent Quantum Lab has recently introduced a new molecular dataset, called Alchemy, to facilitate the development of new machine learning models useful for chemistry and materials science.  
The dataset lists 12 quantum mechanical properties of 130,000+ organic molecules comprising up to 12 heavy atoms (C, N, O, S, F and Cl), sampled from the [GDBMedChem][] database. These properties have been calculated using the open-source computational chemistry program Python-based Simulation of Chemistry Framework ([PySCF][]).

Entry Deadline:

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

## Fashion IQ Challenge ##

[https://competitions.codalab.org/competitions/23391][https_competitions.codalab.org_competitions_23391]

June 1 - Sept 30, 2019 // Host by [CodaLab][CodaLab 1] & [ICCV 2019][ICCV 2019 2] & [Github][] // Prize: NaN

**Note**: Fashion IQ is a new dataset we contribute to the research community to facilitate research on natural language based interactive image retrieval

Entry Deadline:

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

## MicroNet Challenge @NeurIPS 2019 ##

[https://micronet-challenge.github.io/][https_micronet-challenge.github.io]

June 1, 2018 - Dec 13, 2019 // Host by [NeurIPS 2019][NeurIPS 2019 2] // Prize: NaN

**Note**: The competition consists of three different tasks. Contestants are free to submit entries for one, two, or all three tasks. Contestants are allowed to enter up to three models for each task, but will be ranked according to their top entry in each task. Entries can only be trained on the training data for the task they are entered in. No pre-training, or use of auxiliary data is allowed.  
[ImageNet Classification][]: The de facto standard dataset for image classification. The dataset is composed of 1,281,167 training images and 50,000 development images. Entries are required to achieve 75% top-1 accuracy on the public test set.  
[CIFAR-100 Classification][]: A widely popular image classification dataset of small images. The dataset is composed of 50,000 training images and 10,000 development images. Entries are required to achieve 80% top-1 accuracy on the test set.  
[WikiText-103 Language Modeling][]: A language modeling dataset that emphasizes long-term dependencies. Entries will perform the standard language modeling task, predicting the next token from the current one. The dataset is composed of 103 million training words, 217 thousand development words, and 245 thousand testing words. Entries should use the standard word-level vocabulary of 267,735 tokens. Entries are required to achieve a word-level perplexity below 35 on the test set.

Entry Deadline:

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

## Multi-domain Task-Completion Dialog Challenge \[DSTC 8\] ##

[https://www.microsoft.com/en-us/research/project/multi-domain-task-completion-dialog-challenge/][https_www.microsoft.com_en-us_research_project_multi-domain-task-completion-dialog-challenge]

June 17 - Oct 6, 2019 // Host by [CodaLab][CodaLab 1] & [DSTC8][] // Prize: NaN

**Note**: As part of the Eighth Dialog System Technology Challenge ([DSTC8][]), Microsoft Research and Tsinghua University are hosting a track intended to foster progress in two important aspects of dialog systems: dialog complexity and scaling to new domains. For this DSTC8 track, there are two tasks you can compete in (see below). The challenge runs from June 17, 2019 – October 6, 2019.  
Participants will build an [end-to-end multi-domain][] dialog system for tourist information desk settings.  
Participants will develop [fast adaptation][https_www.microsoft.com_en-us_research_project_multi-domain-task-completion-dialog-challenge] methods for building a conversation model that generates appropriate domain-specific user responses to an incomplete dialog history.

Entry Deadline:

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

## Endoscopic Vision Challenge 2019 ##

[https://endovis.grand-challenge.org/Endoscopic\_Vision\_Challenge/][https_endovis.grand-challenge.org_Endoscopic_Vision_Challenge]

June 5 - Oct 13, 2019 // Host by [Grand Challenges][] // Prize: NaN

**Note**: As a vision CAI challenge at MICCAI, our aim is to provide a formal framework for evaluating the current state of the art, gather researchers in the field and provide high quality data with protocols for validating endoscopic vision algorithms.  
EndoVis 2019 Sub-challenges:  
[Surgical Workflow and Skill Analysis][]  
[Stereo Correspondence and Reconstruction of Endoscopic Data][]

Entry Deadline:

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

## Graph Golf: The Order/degree Problem Competition ##

[http://research.nii.ac.jp/graphgolf/][http_research.nii.ac.jp_graphgolf]

05-13 ~ 11-26, 2019 // Host by [CodaLab][CodaLab 8] & [CANDAR 2019][] // Prize: NaN

**Note**: **Find a graph that has smallest diameter & average shortest path length given an order and a degree.**  
Graph Golf is an international competition of the order/degree problem since 2015. It is conducted with the goal of making a catalog of smallest-diameter graphs for every order/degree pair. Anyone in the world can take part in the competition by submitting a graph. Outstanding authors are awarded in CANDAR 2019, an international conference held in Nagasaki, Japan, in November 2019.

Entry Deadline:

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

## The Animal-AI Olympics ##

[http://animalaiolympics.com/][http_animalaiolympics.com]

April - December 2019 // Host by [NeurIPS 2019][NeurIPS 2019 2] // Prize: $10,000+

**Note**: 基于Unity ML Agents Toolkit的动物认知-AI 挑战  
This competition pits our best AI approaches against the animal kingdom to determine if the great successes of AI are now ready to compete with the great successes of evolution at their own game.

Entry Deadline:

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

## EPIC-Kitchens Action Anticipation ##

[https://competitions.codalab.org/competitions/20115][https_competitions.codalab.org_competitions_20115]

July 3, 2018 - Nov. 22 2019 // Host by [CodaLab][CodaLab 1] & [EPIC-KITCHENS 2018][] // Prize: NaN

**Note**: The **largest dataset in first-person (egocentric) vision**; multi-faceted **non-scripted** recordings in native environments - i.e. the wearers’ homes, capturing all daily activities in the kitchen over multiple days.  
[Action-Recognition Challenge][https_competitions.codalab.org_competitions_20115]  
[Action-Anticipation Challenge][]  
[Object-Detection Challenge][]

Entry Deadline:

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

## Geopolitical Forecasting \[GF\] Challenge 2 ##

[https://www.herox.com/IARPAGFChallenge2][https_www.herox.com_IARPAGFChallenge2]

April 4, 2018 - Feb. 1, 2020 // Host by [Herox][] // Prize: $250,000

**Note**: Solvers, whether individuals or teams, will create innovative solutions and methods to produce forecasts to a set of more than 300 questions referred to as Individual Forecasting Problems (IFPs), released regularly over the course of the nine-month Challenge.

Entry Deadline:

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

## ModaNet Fashion Understanding Challenge ##

[https://evalai.cloudcv.org/web/challenges/challenge-page/151][https_evalai.cloudcv.org_web_challenges_challenge-page_151]

Oct 1, 2018 - Dec 11, 2019 // Host by [EvalAI][EvalAI 2] // Prize: NaN

**Note**: In this challenge, we evaluate model performance for three tasks, object detection, semantic segmentation and instance segmentation. You can participate all tasks or any one of them by choosing which results to be included in your submission.

Entry Deadline:

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

## 「二分类算法」提供银行精准营销解决方案 | 练习赛 ##

[https://www.kesci.com/home/competition/5c234c6626ba91002bfdfdd3][https_www.kesci.com_home_competition_5c234c6626ba91002bfdfdd3]

2018年12月29日 - 2019年12月29日 // Host by [Kesci][] // Prize: NaN

**Note**: 本练习赛的数据，选自UCI机器学习库中的「银行营销数据集(Bank Marketing Data Set)」

Entry Deadline:

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

## SPIE-AAPM-NCI BreastPathQ: Cancer Cellularity Challenge 2019 ##

[http://spiechallenges.cloudapp.net/competitions/14][http_spiechallenges.cloudapp.net_competitions_14]

Oct. 15, 2018 - Dec. 31, 2019 // Host by [ISBI 2019][] & [Grand Challenges][Grand Challenges 1] &[cloudapp.net][] // Prize: NaN

**Note**: Participants will be tasked to develop an automated method for analyzing histology patches extracted from whole slide images and assign a score reflecting cancer cellularity in each.

Entry Deadline:

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

## Optimizing well-being at work ##

[https://challengedata.ens.fr/challenges/15][https_challengedata.ens.fr_challenges_15]

Jan. 1, 2019 - Jan. 1, 2020 // Host by [Challenge data][] & [Oze Energies][] // Prize: NaN

**Note**: This challenge proposes to develop machine learning based approaches so as to predict individuals’ comfort model using several **time series** of environmental data obtained from sensors in a large building. The objective is to learn a classifier that uses these time series as inputs to predict the associated comfort class computed as an average of the comfort classes of all individuals in the building, assumed to experience the same environmental conditions.

Entry Deadline:

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

## Drug-related questions classification ##

[https://challengedata.ens.fr/challenges/17][https_challengedata.ens.fr_challenges_17]

Jan. 1, 2019 - Jan. 1, 2020 // Host by [Challenge data][] & [Posos][] // Prize: NaN

**Note**: The goal of Posos challenge is to predict for each question the associated intent.

Entry Deadline:

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

## Detecting breast cancer metastases ##

[https://challengedata.ens.fr/challenges/18][https_challengedata.ens.fr_challenges_18]

Jan. 1, 2019 - Jan. 1, 2020 // Host by [Challenge data][] & [OWKIN][] // Prize: NaN

**Note**: The challenge proposed by Owkin is a **weakly-supervised binary classification problem** : predict whether a patient has any metastase in its lymph node or not, given its slide.

Entry Deadline:

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

## Building Claim Prediction ##

[https://challengedata.ens.fr/challenges/19][https_challengedata.ens.fr_challenges_19]

Jan. 1, 2019 - Jan. 1, 2020 // Host by [Challenge data][] & [Generali][] // Prize: NaN

**Note**: The goal of the challenge is to predict if a building will have an insurance claim during a certain period. You will have to predict a probability of having at least one claim over the insured period of a building.

Entry Deadline:

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

## Crack the neural code of the brain ##

[https://challengedata.ens.fr/challenges/14][https_challengedata.ens.fr_challenges_14]

Jan. 1, 2019 - Jan. 1, 2020 // Host by [Challenge data][] & [GNT ENS][] // Prize: NaN

**Note**: The challenge goal is to classify the brain activity state of an animal based on spiking activity patterns of its individual neurons.

Entry Deadline:

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

## Prediction of Sharpe ratio for blends of quantitative strategies ##

[https://challengedata.ens.fr/challenges/13][https_challengedata.ens.fr_challenges_13]

Jan. 1, 2019 - Jan. 1, 2020 // Host by [Challenge data][] & [Napoleon X][] // Prize: NaN

**Note**: The problem is a prediction challenge that aims at helping the Company to build an optimal blend of quantitative strategies, given a set of such strategies.

Entry Deadline:

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

## Historical consumption regression for electricity supply pricing ##

[https://challengedata.ens.fr/challenges/12][https_challengedata.ens.fr_challenges_12]

Jan. 1, 2019 - Jan. 1, 2020 // Host by [Challenge data][] & [BCM Energy][] // Prize: NaN

**Note**: The goal of the challenge is to predict, based on the analysis of the correlation of a year of consumption and weather training data, the electricity consumption of two given sites for a test year.

Entry Deadline:

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

## Predict brain deep sleep slow oscillation ##

[https://challengedata.ens.fr/challenges/10][https_challengedata.ens.fr_challenges_10]

Jan. 1, 2019 - Jan. 1, 2020 // Host by [Challenge data][] & [Dreem][] // Prize: NaN

**Note**: In this dataset, we try to predict whether or not a slow oscillation will be followed by another one in sham condition, i.e. without any stimulation.

Entry Deadline:

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

## Spatiotemporal PM10 concentration prediction ##

[https://challengedata.ens.fr/challenges/7][https_challengedata.ens.fr_challenges_7]

Jan. 1, 2019 - Jan. 1, 2020 // Host by [Challenge data][] & [Plume Labs][] // Prize: NaN

**Note**: In order to provide air quality forecasts, Plume Labs has built a unique database with readings collected by monitoring stations all over the world. The problem we submit consists in predicting the PM10 readings of some air quality monitoring stations using the readings provided by the monitoring stations nearby as well as urban features.

Entry Deadline:

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

## Dynamic Profile Forecasting ##

[https://challengedata.ens.fr/challenges/6][https_challengedata.ens.fr_challenges_6]

Jan. 1, 2019 - Jan. 1, 2020 // Host by [Challenge data][] & [Enedis][] // Prize: NaN

**Note**: This challenge is about forecasting dynamic profiles values from their past values and all the components of [Enedis’ Half hourly Electrical Balancing][Enedis_ Half hourly Electrical Balancing].

Entry Deadline:

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

## Solve 2x2x2 Rubik’s cube ##

[https://challengedata.ens.fr/challenges/20][https_challengedata.ens.fr_challenges_20]

Jan. 1, 2019 - Jan. 1, 2020 // Host by [Challenge data][] & [LumenAI][] // Prize: NaN

**Note**: The goal is to design an automatic Rubik’s analyzer that estimates the current length of the shortest path to the solution.

Entry Deadline:

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

## Exotic pricing with multidimensional non-linear interpolation ##

[https://challengedata.ens.fr/challenges/9][https_challengedata.ens.fr_challenges_9]

Jan. 1, 2019 - Jan. 1, 2020 // Host by [Challenge data][] & [Natixis][] // Prize: NaN

**Note**: The purpose of the challenge is to use a training set of 1 million prices to learn how to price a specific type of instruments described by 23 parameters by nonlinear interpolation on these prices.

Entry Deadline:

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

## Screening and Diagnosis of esophageal cancer from in-vivo microscopy images ##

[https://challengedata.ens.fr/challenges/11][https_challengedata.ens.fr_challenges_11]

Jan. 1, 2019 - Jan. 1, 2020 // Host by [Challenge data][] & [Mauna Kea Technologies][] // Prize: NaN

**Note**: The goal of this challenge is to build an image classifier to assist physicians in the screening and diagnosis of esophageal cancer.

Entry Deadline:

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

## Prediction of daily stock movements on the US market ##

[https://challengedata.ens.fr/challenges/16][https_challengedata.ens.fr_challenges_16]

Jan. 1, 2019 - Jan. 1, 2020 // Host by [Challenge data][] & [CFM][] // Prize: NaN

**Note**: The **goal** of this challenge is to **predict the sign of the returns** (= price change over some time interval) **at the end of about 700 days for about 700 stocks**.

Entry Deadline:

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

## MEMENTO: MRI White Matter Reconstruction ##

[https://my.vanderbilt.edu/memento/][https_my.vanderbilt.edu_memento]

March 7, 2019 - March 4, 2020 // Host by [ISBI 2019][] // Prize: NaN

**Note**: **This will be a 2-year challenge.**  
We aim to host 3 sub-challenges evaluating our current ability to:  
(1) predict unseen signal (signal representation; [sub-challenge \#1][sub-challenge _1])  
(2) estimate microstructural measures (signal modeling; [sub-challenge \#2][sub-challenge _2])  
(3) evaluate sensitivity and specificity of potential biomarkers (biomarker evaluation; [sub-challenge \#3][sub-challenge _3]).

Entry Deadline:

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

## Propensity to Fund Mortgages ##

[https://www.crowdanalytix.com/contests/propensity-to-fund-mortgages][https_www.crowdanalytix.com_contests_propensity-to-fund-mortgages]

25 APR 2019 - 6 JUN 2019 // Host by [CrowdANALYTIX][] // Prize: $10000

**Note**: Develop a model to predict, given mortgage application information, whether the mortgage will be funded or not.  
To predict whether a mortgage will be funded using only this application data, certain leading factors driving the loan’s ultimate status will be identified. Solvers will discover the specific aspects of the dataset that have the greatest impact, and build a model based on this information.

Entry Deadline:

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

## Identify Characters from Product Images ##

[https://www.crowdanalytix.com/contests/identify-characters-from-product-images][https_www.crowdanalytix.com_contests_identify-characters-from-product-images]

12 MAY 2019 - 9 JUL 2019 // Host by [CrowdANALYTIX][] // Prize: NaN

**Note**: Identify the characters from product image from a list of 42 possible values.  
While using machine learning to perform image recognition is currently one of the most popular use cases, in some cases, the existing large-scale models are too broad to be effective for specific business use cases. In this contest we will use a data driven approach to identify the “characters” in an image (product images).

Entry Deadline:

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

## KiTS19 Challenge ##

[https://kits19.grand-challenge.org/][https_kits19.grand-challenge.org]

March 15 - August 2, 2019 // Host by [Grand Challenges][Grand Challenges 1] // Prize: NaN

**Note**: The goal of this challenge is to accelerate the development of reliable kidney and kidney tumor semantic segmentation methodologies.

Entry Deadline:

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

## PAIP 2019 Challenge ##

[https://paip2019.grand-challenge.org/][https_paip2019.grand-challenge.org]

April 15 - September 2, 2019 // Host by [Grand Challenges][Grand Challenges 1] & [MICCAI 2019][] // Prize: NaN

**Note**: The goal of the challenge is to evaluate new and existing algorithms for automated detection of liver cancer in whole-slide images (WSIs). There are two tasks and therefore two leaderboards for evaluating the performance of the algorithms. Participants can choose to join both or either tasks according to their interests.  
Task 1: Liver Cancer Segmentation  
Task 2: Viable Tumor Burden Estimation

Entry Deadline:

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

## ImageNet Object Localization Challenge ##

[https://www.kaggle.com/c/imagenet-object-localization-challenge][https_www.kaggle.com_c_imagenet-object-localization-challenge]

Now - December 31 2029 // Host by [Kaggle][] // Prize: NaN

**Note**: Identify the objects in images

Entry Deadline:

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

## nocaps ##

[https://nocaps.org/][https_nocaps.org]

Feb 8, 2019 - Apr 26, 2099 // Host by [EvalAI][EvalAI 3] // Prize: NaN

**Note**: Image captioning models have achieved impressive results on datasets containing limited visual concepts and large amounts of paired image-caption training data. However, if these models are to ever function in the wild, a much larger variety of visual concepts must be learned, ideally from less supervision. To encourage the development of image captioning models that can learn visual concepts from alternative data sources, such as object detection datasets, we present the first large-scale benchmark for this task. Dubbed nocaps, for novel object captioning at scale, our benchmark consists of 166,100 human-generated captions describing 15,100 images from the Open Images validation and test sets. The associated training data consists of COCO image-caption pairs, plus Open Images imagelevel labels and object bounding boxes. Since Open Images contains many more classes than COCO, nearly 400 object classes seen in test images have no or very few associated training captions (hence, nocaps). We extend existing novel object captioning models to establish strong baselines for this benchmark and provide analysis to guide future work on this task.

Entry Deadline:

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

## nuScenes detection challenge ##

[https://www.nuscenes.org/][https_www.nuscenes.org]

Apr 1, 2019 - Jan 1, 2099 // Host by [EvalAI][EvalAI 4] & [CVPR 2019][] // Prize: NaN

**Note**: The nuScenes dataset is a large-scale autonomous driving dataset.

Entry Deadline:

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

## Predict Future Sales ##

[https://www.kaggle.com/c/competitive-data-science-predict-future-sales][https_www.kaggle.com_c_competitive-data-science-predict-future-sales]

No deadline // Host by [Kaggle][] // Prize: NaN

**Note**: Final project for “How to win a data science competition” Coursera course

No Deadline.

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

## Evaluating grammatical error corrections ##

[https://competitions.codalab.org/competitions/15475][https_competitions.codalab.org_competitions_15475]

Nov. 23, 2016 - Never // Host by [CodaLab][CodaLab 1] // Prize: NaN

No Deadline.

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

## Lexical Semantic Change Detection in German ##

[https://competitions.codalab.org/competitions/23563][https_competitions.codalab.org_competitions_23563]

July 1 - Never // Host by [CodaLab][https_competitions.codalab.org_competitions_23563] // Prize: NaN

**Note**: Given two corpora Ca and Cb, rank all target words according to their degree of lexical semantic change between Ca and Cb as annotated by human judges. (Higher rank means higher change.)

No Deadline.

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

## YouCook2-BoundingBoxes Video Object Grounding Task ##

[http://youcook2.eecs.umich.edu/][http_youcook2.eecs.umich.edu]

June 24, 2019 - Never // Host by [CodaLab][CodaLab 9] & [Github][Github 1] // Prize: NaN

**Note**: YouCook2 is the largest task-oriented, instructional video dataset in the vision community. It contains 2000 long untrimmed videos from 89 cooking recipes; on average, each distinct recipe has 22 videos. The procedure steps for each video are annotated with temporal boundaries and described by imperative English sentences (see the example below).

No Deadline.

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

## Oil Radish Semantic Segmentation and Yield Estimation Challenges ##

[https://competitions.codalab.org/competitions/23386][https_competitions.codalab.org_competitions_23386]

June 1, 2019 - Never // Host by [CodaLab][CodaLab 1] & [CVPPP 2019][] & [CVPR 2019][] // Prize: NaN

**Note**: The challenges associated with the dataset are the Semantic Segmentation challenge and the Yield Estimation challenge. In the Semantic Segmentation challenge, participants must perform pixel-wise classifiction on a subset of the labelled images. In the Yield Estimation challenge, participants must estimate the oil radish yield of same subset of labelled images.

No Deadline.

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

## Challenge: Learning To Drive (L2D) ##

[https://competitions.codalab.org/competitions/23245][https_competitions.codalab.org_competitions_23245]

June 1, 2019 - Never // Host by [CodaLab][CodaLab 1] & [ICCV 2019][] // Prize: NaN

**Note**: Challenge participants need to develop driving models that can drive most similar to the human driver that recorded the dataset.

No Deadline.

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

## Mobile age group classification ##

[https://competitions.codalab.org/competitions/22946][https_competitions.codalab.org_competitions_22946]

May. 17, 2019 - Never // Host by [CodaLab][CodaLab 1] // Prize: NaN

**Note**: This is an EE331 competition leaderboard for Mobile age group classification. It consists of 157K datasamples with 85 various features and age group label (ranging from 1 to 6). The data is splitted into train : validation : test sset with 70 : 20 : 10 ratio.

No Deadline.

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

## Perfect Pitching Simulator ##

[https://fastballs.wordpress.com/category/pitchfx-glossary/][https_fastballs.wordpress.com_category_pitchfx-glossary]

May. 17, 2019 - Never // Host by [CodaLab][CodaLab 10] // Prize: NaN

**Note**: Perfect Pitching Simulator!

No Deadline.

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

## ActivityNet-Entities Object Localization Task ##

[https://github.com/facebookresearch/ActivityNet-Entities][https_github.com_facebookresearch_ActivityNet-Entities]

May 7, 2019 - Never // Host by [CodaLab][CodaLab 11] & [CVPR 2019][] // Prize: NaN

**Note**: ActivityNet-Entities, is based on the video description dataset ActivityNet Captions and augments it with 158k bounding box annotations, each grounding a noun phrase (NP). Here we release the complete set of NP-based annotations as well as the pre-processed object-based annotations.  
please see our dataset [repo][https_github.com_facebookresearch_ActivityNet-Entities], code [repo][], and CVPR 2019 oral [paper][].

No Deadline.

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

## YouCook2 Dense Video Captioning ##

[http://youcook2.eecs.umich.edu/][http_youcook2.eecs.umich.edu]

May 6, 2019 - Never // Host by [CodaLab][CodaLab 12] // Prize: NaN

**Note**: YouCook2 is currently suitable for video-language research, weakly-supervised activity and object recognition in video, common object and action discovery across videos and procedure learning.

No Deadline.

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

## The First Australian Centre for Robotic Vision (ACRV) Challenge ##

[https://competitions.codalab.org/competitions/20940][https_competitions.codalab.org_competitions_20940]

Dec. 1, 2018 - Never // Host by [CodaLab][CodaLab 1] // Prize: NaN

**Note**: The challenge consists in building an AI agent that can play efficiently and win simplified text-based games using [TextWorld][].

No Deadline.

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

## TVQA Test Public Evaluation (w/timestamp) Beta ##

[https://competitions.codalab.org/competitions/20686][https_competitions.codalab.org_competitions_20686]

Nov. 16, 2018 - Never // Host by [CodaLab][CodaLab 1] & [TVQA][] // Prize: NaN

**Note**: This portal is only used for models that used ‘ts’ (timestamp annotations)  
TVQA is a large-scale video QA dataset based on 6 popular TV shows (Friends, The Big Bang Theory, How I Met Your Mother, House M.D., Grey’s Anatomy, Castle).

No Deadline.

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

## IWCS-2019 shared task: DRS Parsing ##

[https://competitions.codalab.org/competitions/20220][https_competitions.codalab.org_competitions_20220]

Feb. 25, 2018 - Never // Host by [CodaLab][CodaLab 1] & [IWCS-2019][] // Prize: NaN

**Note**: The shared task on DRS parsing will be co-located with IWCS-2019 held in Gothenburg, Sweden on 23-27 May.

No Deadline.

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

## Intuitive Physics Challenge 2019 ##

[https://competitions.codalab.org/competitions/20574][https_competitions.codalab.org_competitions_20574]

Oct. 1, 2018 - Never // Host by [CodaLab][CodaLab 1] & [IntPhys][] // Prize: NaN

No Deadline.

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

## SemEval-2019 ##

[http://alt.qcri.org/semeval2019/index.php?id=tasks][http_alt.qcri.org_semeval2019_index.php_id_tasks]

Now - Never // Host by [CodaLab][CodaLab 1] & [SemEval-2019][] // Prize: NaN

**Note**:  
**Frame semantics and semantic parsing:**  
[Task 1: Cross-lingual Semantic Parsing with UCCA][Task 1_ Cross-lingual Semantic Parsing with UCCA]  
[Task 2: Unsupervised Lexical Semantic Frame Induction][Task 2_ Unsupervised Lexical Semantic Frame Induction]  
**Opinion, emotion and abusive language detection**  
[Task 3: EmoContext: Contextual Emotion Detection in Text][Task 3_ EmoContext_ Contextual Emotion Detection in Text]  
[Task 4: Hyperpartisan News Detection][Task 4_ Hyperpartisan News Detection]  
[Task 5: HatEval: Multilingual Detection of Hate Speech Against Immigrants and Women in Twitter][Task 5_ HatEval_ Multilingual Detection of Hate Speech Against Immigrants and Women in Twitter]  
[Task 6: OffensEval: Identifying and Categorizing Offensive Language in Social Media<\\a>  
**Fact vs fiction**  
][Task 6_ OffensEval_ Identifying and Categorizing Offensive Language in Social Media_a_ _Fact vs fiction_][Task 7: RumourEval 2019: Determining Rumour Veracity and Support for Rumours][Task 7_ RumourEval 2019_ Determining Rumour Veracity and Support for Rumours]  
[Task 8: Fact Checking in Community Question Answering Forums][Task 8_ Fact Checking in Community Question Answering Forums]  
**Information extraction and question answering**  
[Task 9: Suggestion Mining from Online Reviews and Forums][Task 9_ Suggestion Mining from Online Reviews and Forums]  
[Task 10: Math Question Answering][Task 10_ Math Question Answering]  
**NLP for scientific applications**  
[Task 12: Toponym Resolution in Scientific Papers][Task 12_ Toponym Resolution in Scientific Papers]

No Deadline.

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

## WiC\_competition ##

[https://competitions.codalab.org/competitions/20010][https_competitions.codalab.org_competitions_20010]

Aug. 18, 2018 - Never // Host by [CodaLab][CodaLab 1] // Prize: NaN

**Note**: You can get all the information and data at [https://pilehvar.github.io/wic][https_pilehvar.github.io_wic]

No Deadline.

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

## OxUvA Long-Term Tracking Challenge ##

[https://competitions.codalab.org/competitions/19529][https_competitions.codalab.org_competitions_19529]

July 1, 2018 - Never // Host by [CodaLab][CodaLab 1] & [ECCV 2018][] // Prize: NaN

**Note**: “We introduce a new video dataset and benchmark to assess single-object tracking algorithms.”

No Deadline.

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

## Evergreen: Automatically detect drill core tray outlines in core photography ##

[https://unearthed.solutions/u/competitions/evergreen/get-2-the-core][https_unearthed.solutions_u_competitions_evergreen_get-2-the-core]

June 27, 2019 - Never // Host by [Unearthed][] // Prize: NaN

**Note**: This global online competition invites innovators from around the world to build an algorithm that can determine and map the spatial extents of the core tray and then the individual rows contained within.

No Deadline.

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

## Evergreen: Identify depth measurements in core images ##

[https://unearthed.solutions/u/competitions/evergreen/get-2-core-ii-revenge-depths][https_unearthed.solutions_u_competitions_evergreen_get-2-core-ii-revenge-depths]

June 27, 2019 - Never // Host by [Unearthed][] // Prize: NaN

**Note**: This is an online competition inviting companies and individuals from around the world to provide a solution that can correctly identify recorded depths within a core photograph.

No Deadline.

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

## Evergreen: Reduce water usage in gold processing through tailings density prediction ##

[https://unearthed.solutions/u/competitions/evergreen/hydrosaver][https_unearthed.solutions_u_competitions_evergreen_hydrosaver]

June 27, 2019 - Never // Host by [Unearthed][] // Prize: NaN

**Note**: This global online competition invites data scientists and innovators from around the world to develop a prediction model for tailings density (and therefore water consumption) in Newcrest’s gold processing operations.

No Deadline.

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

## Lymphocyte Detection ##

[https://lyon19.grand-challenge.org/][https_lyon19.grand-challenge.org]

under construction // Host by [Grand Challenges][Grand Challenges 1] // Prize: NaN

**Note**: Dataset contains manual annotations as a ground truth data.

No Deadline.

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

## DRIVE: Digital Retinal Images for Vessel Extraction ##

[https://drive.grand-challenge.org/][https_drive.grand-challenge.org]

No deadline // Host by [Grand Challenges][Grand Challenges 1] // Prize: NaN

**Note**: The DRIVE database has been established to enable comparative studies on segmentation of blood vessels in retinal images. Develop a system to automatically segment vessels in human retina fundus images.

No Deadline.

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

## PatchCamelyon ##

[https://github.com/basveeling/pcam][https_github.com_basveeling_pcam]

No deadline // Host by [Grand Challenges][] // Prize: NaN

**Note**: The PatchCamelyon benchmark is a new and challenging image classification dataset. It consists of 327.680 color images (96 x 96px) extracted from histopathologic scans of lymph node sections. Each image is annoted with a binary label indicating presence of metastatic tissue. PCam provides a new benchmark for machine learning models: bigger than CIFAR10, smaller than imagenet, trainable on a single GPU.

No Deadline.

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

## The Large Scale Vertebrae Segmentation Challenge (VerSe2019) ##

[https://verse2019.grand-challenge.org/][https_verse2019.grand-challenge.org]

May 16 - TBA // Host by [Grand Challenges][] // Prize: NaN

**Note**: Spine or vertebral segmentation is a crucial step in all applications regarding automated quantification of spinal morphology and pathology. With the advent of deep learning, for such a task on computed tomography (CT) scans, a big and varied data is a primary sought-after resource.  
Task 1: Vertebra Labelling  
Task 2: Vertebra Segmentation

No Deadline.

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

## Vision and Language Navigation ##

[https://evalai.cloudcv.org/web/challenges/challenge-page/97][https_evalai.cloudcv.org_web_challenges_challenge-page_97]

Mar 13, 2018 - Dec 31, 2099 // Host by [EvalAI][EvalAI 2] // Prize: NaN

**Note**: The challenge requires an autonomous agent to follow a natural language navigation instruction to navigate to a goal location in a previously unseen real-world building.

No Deadline.

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

## VizWiz Challenge 2018 ##

[http://vizwiz.org/data/\#challenge][http_vizwiz.org_data_challenge]

Jun 20, 2018 - Jun 22, 2100 // Host by [EvalAI][EvalAI 5] // Prize: NaN

**Note**: Our proposed challenge addresses the following two tasks for this dataset: (1) predict the answer to a visual question and (2) predict whether a visual question cannot be answered.

No Deadline.

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

## SQuAD2.0: The Stanford Question Answering Dataset ##

[https://rajpurkar.github.io/SQuAD-explorer/][https_rajpurkar.github.io_SQuAD-explorer]

No deadline // Host by [Stanford NLP Group][] // Prize: NaN

**Note**: **S**tanford **Q**uestion **A**nswering **D**ataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable.

No Deadline.

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

## CoQA: A Conversational Question Answering Challenge ##

[https://stanfordnlp.github.io/coqa/][https_stanfordnlp.github.io_coqa]

No deadline // Host by [Stanford NLP Group][] // Prize: NaN

**Note**: CoQA is a large-scale dataset for building **Co**nversational **Q**uestion **A**nswering systems. The goal of the CoQA challenge is to measure the ability of machines to understand a text passage and answer a series of interconnected questions that appear in a conversation. CoQA is pronounced as [coca][].

No Deadline.

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

## Unrestricted Adversarial Examples Challenge ##

[https://github.com/google/unrestricted-adversarial-examples][https_github.com_google_unrestricted-adversarial-examples]

No deadline // Host by [Google AI][] // Prize: NaN

**Note**: A community-based challenge to incentivize and measure progress towards the goal of zero confident classification errors in machine learning models.  
(不受限对抗样本挑战) [The project on Github][https_github.com_google_unrestricted-adversarial-examples]

No Deadline.

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

## The Natural Language Decathlon: A Multitask Challenge for NLP ##

[http://decanlp.com/][http_decanlp.com]

No deadline // Host by [salesforce][] // Prize: NaN

**Note**: The Natural Language Decathlon is a multitask challenge that spans ten tasks: question answering (**SQuAD**), machine translation (**IWSLT**), summarization (**CNN/DM**), natural language inference (**MNLI**), sentiment analysis (**SST**), semantic role labeling(**QA‑SRL**), zero-shot relation extraction (**QA‑ZRE**), goal-oriented dialogue (**WOZ**), semantic parsing (**WikiSQL**), and commonsense reasoning (**MWSC**).

[DataSciComp]: https://iphysresearch.github.io/DataSciComp/
[iphysresearch_DataSciComp]: https://github.com/iphysresearch/DataSciComp
[ApacheCN]: https://www.apachecn.org/
[Link 1]: https://weibo.com/u/6326715527
[Link 2]: https://www.zhihu.com/people/apachecn
[CSDN]: https://blog.csdn.net/wizardforcel/article/category/8437073
[Link 3]: https://www.jianshu.com/c/4ee721d0c474
[OSChina]: https://my.oschina.net/repine/
[Link 4]: https://www.cnblogs.com/wizardforcel/category/1352397.html
[CareerVillage.org]: http://CareerVillage.org
[https_www.kaggle.com_c_data-science-for-good-careervillage]: https://www.kaggle.com/c/data-science-for-good-careervillage
[Kaggle]: https://www.kaggle.com/
[https_signate.jp_competitions_168]: https://signate.jp/competitions/168
[SIGNATE]: https://signate.jp/
[https_www.biendata.com_competition_ccks_2019_1]: https://www.biendata.com/competition/ccks_2019_1/
[Biendata]: https://www.biendata.com/
[https_www.biendata.com_competition_ccks_2019_el]: https://www.biendata.com/competition/ccks_2019_el/
[https_www.4paradigm.com_competition_kddcup2019]: https://www.4paradigm.com/competition/kddcup2019
[4Paradigm]: https://www.4paradigm.com/
[ChaLearn]: http://chalearn.org/
[CodaLab]: https://competitions.codalab.org/competitions/21948
[https_biendata.com_competition_smpecisa2019]: https://biendata.com/competition/smpecisa2019/
[https_sites.google.com_view_emotiw2019]: https://sites.google.com/view/emotiw2019
[ICMI 2019]: https://icmi.acm.org/2019/
[https_competitions.codalab.org_competitions_21226]: https://competitions.codalab.org/competitions/21226
[CodaLab 1]: https://competitions.codalab.org/
[KONVENS 2019]: https://dgfs.de/en/cl/konvens.html
[https_sigir-ecom.github.io_data-task.html]: https://sigir-ecom.github.io/data-task.html
[EvalAI]: https://evalai.cloudcv.org/web/challenges/challenge-page/361/
[The 2019 SIGIR Workshop On eCommerce]: https://sigir-ecom.github.io/index.html
[https_tianchi.aliyun.com_competition_entrance_231717_introduction]: https://tianchi.aliyun.com/competition/entrance/231717/introduction
[Link 5]: https://tianchi.aliyun.com/
[https_tianchi.aliyun.com_competition_entrance_231714]: https://tianchi.aliyun.com/competition/entrance/231714/
[https_www.biendata.com_competition_ccks_2019_6]: https://www.biendata.com/competition/ccks_2019_6/
[https_www.register.extension.iastate.edu_mlcas2019_competition]: https://www.register.extension.iastate.edu/mlcas2019/competition
[CodaLab 2]: https://competitions.codalab.org/competitions/23177
[MLCAS 2019]: https://www.register.extension.iastate.edu/mlcas2019
[https_www.kaggle.com_c_recursion-cellular-image-classification]: https://www.kaggle.com/c/recursion-cellular-image-classification
[NeurIPS 2019]: https://nips.cc/Conferences/2019/CallForCompetitions
[https_tianchi.aliyun.com_competition_entrance_231724]: https://tianchi.aliyun.com/competition/entrance/231724/
[https_datahack.analyticsvidhya.com_contest_build-a-recommendation-engine-powered-by-ibm-cloud]: https://datahack.analyticsvidhya.com/contest/build-a-recommendation-engine-powered-by-ibm-cloud/
[Analytics Vidhya]: https://datahack.analyticsvidhya.com/contest/all/?
[http_wider-challenge.org_2019.html]: http://wider-challenge.org/2019.html
[ICCV 2019]: http://iccv2019.thecvf.com/
[Link 6]: https://www.sensetime.com/
[Amazon]: https://aws.amazon.com/rekognition/
[First WIDER Challenge Workshop]: http://wider-challenge.org/2018.html
[WIDER Face Detection]: http://wider-challenge.org/comingsoon.html
[WIDER Pedestrian Detection]: https://competitions.codalab.org/competitions/22852
[WIDER Cast Search by Portrait]: https://competitions.codalab.org/competitions/22833
[WIDER Person Search by Language]: https://competitions.codalab.org/competitions/22864
[https_opendata.sz.gov.cn_sodic2019]: https://opendata.sz.gov.cn/sodic2019/
[Link 7]: https://opendata.sz.gov.cn/
[HUAWEI]: https://www.huawei.com/
[https_ai.ppdai.com_mirror_goToMirrorDetail_mirrorId_17]: https://ai.ppdai.com/mirror/goToMirrorDetail?mirrorId=17
[AI]: https://ai.ppdai.com/mirror/show
[DC]: https://www.dcjingsai.com/common/cmpt/%E7%AC%AC%E5%9B%9B%E5%B1%8A%E2%80%9C%E9%AD%94%E9%95%9C%E6%9D%AF%E2%80%9D%E6%95%B0%E6%8D%AE%E5%BA%94%E7%94%A8%E5%A4%A7%E8%B5%9B_%E7%AB%9E%E8%B5%9B%E4%BF%A1%E6%81%AF.html
[https_www.biendata.com_competition_ccks_2019_4]: https://www.biendata.com/competition/ccks_2019_4/
[https_ai.futurelab.tv_contest_detail_4]: https://ai.futurelab.tv/contest_detail/4
[FUTURE.AI]: https://ai.futurelab.tv/
[Link 8]: https://ai.futurelab.tv/contest_detail/6
[Link 9]: https://ai.futurelab.tv/contest_detail/5
[https_sites.google.com_view_cvcreative_deepfashion2_authuser_0]: https://sites.google.com/view/cvcreative/deepfashion2?authuser=0
[CodaLab 3]: https://competitions.codalab.org/competitions
[github]: https://github.com/switchablenorms/DeepFashion2
[Track 1 Clothes Landmark Estimation]: https://competitions.codalab.org/competitions/23095
[Track 2 Clothes Retrieval]: https://competitions.codalab.org/competitions/23098
[https_www.kesci.com_home_competition_5d142d8cbb14e6002c04e14a]: https://www.kesci.com/home/competition/5d142d8cbb14e6002c04e14a
[Kesci]: https://www.kesci.com/
[http_contest.cfdsj.cn_index_care]: http://contest.cfdsj.cn/index/care
[Link 10]: http://cfdsj.cn/
[https_tianyicup.kesci.com]: https://tianyicup.kesci.com/
[https_www.kaggle.com_c_recognizing-faces-in-the-wild_overview_description]: https://www.kaggle.com/c/recognizing-faces-in-the-wild/overview/description
[https_tianchi.aliyun.com_competition_entrance_231716_introduction]: https://tianchi.aliyun.com/competition/entrance/231716/introduction
[https_competitions.codalab.org_competitions_21012]: https://competitions.codalab.org/competitions/21012
[ISBI 2019]: https://biomedicalimaging.org/2019/challenges/
[https_tianchi.aliyun.com_competition_entrance_231718_introduction_spm_5176.12281949.1003.1.3a994c2axCiMpB]: https://tianchi.aliyun.com/competition/entrance/231718/introduction?spm=5176.12281949.1003.1.3a994c2axCiMpB
[https_www.kaggle.com_c_generative-dog-images]: https://www.kaggle.com/c/generative-dog-images
[https_emw.ku.edu.tr_event_challenges-and-opportunities-in-automated-coding-of-contentious-political-events]: https://emw.ku.edu.tr/?event=challenges-and-opportunities-in-automated-coding-of-contentious-political-events
[CodaLab 4]: https://competitions.codalab.org/competitions/22842
[Euro CSS 2019]: http://symposium.computationalsocialscience.eu/2019/
[http_rscup.bjxintong.com.cn]: http://rscup.bjxintong.com.cn/
[Link 11]: http://www.csu.cas.cn/
[Link 12]: http://rscup.bjxintong.com.cn/#/theme/1
[Link 13]: http://rscup.bjxintong.com.cn/#/theme/2
[Link 14]: http://rscup.bjxintong.com.cn/#/theme/3
[Link 15]: http://rscup.bjxintong.com.cn/#/theme/4
[Link 16]: http://rscup.bjxintong.com.cn/#/theme/5
[AI 1]: http://rscup.bjxintong.com.cn/#/theme/6
[https_tianchi.aliyun.com_competition_entrance_231719]: https://tianchi.aliyun.com/competition/entrance/231719/
[Predicting User Behavior Diversities in A Dynamic Interactive Environment]: https://tianchi.aliyun.com/competition/entrance/231719
[Efficient and Novel Item Retrieval for Large-scale Online Shopping Recommendation]: https://tianchi.aliyun.com/competition/entrance/231721
[https_ariel-datachallenge.azurewebsites.net]: https://ariel-datachallenge.azurewebsites.net/
[ECML-PKDD 2019]: http://ecmlpkdd2019.org/
[here]: https://arieldata.blob.core.windows.net/static/ariel_ml_datachallenge_press.pdf
[The first ARIEL Data Challenge]: https://ariel-datachallenge.azurewebsites.net/ML
[A second ARIEL Data Challenge]: https://ariel-datachallenge.azurewebsites.net/retrieval
[A further data analysis challenge]: https://ariel-datachallenge.azurewebsites.net/#
[https_competitions.codalab.org_competitions_23626]: https://competitions.codalab.org/competitions/23626
[https_signate.jp_competitions_162]: https://signate.jp/competitions/162
[https_www.dcjingsai.com]: https://www.dcjingsai.com/
[DC 1]: http://www.dcjingsai.com/
[Link 17]: https://www.xfyun.cn/
[AI_-]: https://www.dcjingsai.com/common/cmpt/AI%E5%BC%80%E5%8F%91%E8%80%85%E5%A4%A7%E8%B5%9B-%E5%B7%A5%E7%A8%8B%E6%9C%BA%E6%A2%B0%E6%A0%B8%E5%BF%83%E9%83%A8%E4%BB%B6%E5%AF%BF%E5%91%BD%E9%A2%84%E6%B5%8B%E6%8C%91%E6%88%98%E8%B5%9B_%E7%AB%9E%E8%B5%9B%E4%BF%A1%E6%81%AF.html
[AI_- 1]: https://www.dcjingsai.com/common/cmpt/AI%E5%BC%80%E5%8F%91%E8%80%85%E5%A4%A7%E8%B5%9B-%E5%A4%A7%E6%95%B0%E6%8D%AE%E5%BA%94%E7%94%A8%E5%88%86%E7%B1%BB%E6%A0%87%E6%B3%A8%E6%8C%91%E6%88%98%E8%B5%9B_%E7%AB%9E%E8%B5%9B%E4%BF%A1%E6%81%AF.html
[AI_- 2]: https://www.dcjingsai.com/common/cmpt/AI%E5%BC%80%E5%8F%91%E8%80%85%E5%A4%A7%E8%B5%9B-%E5%B9%BF%E5%91%8A%E8%90%A5%E9%94%80%E5%8F%8D%E4%BD%9C%E5%BC%8A%E7%AE%97%E6%B3%95%E6%8C%91%E6%88%98%E8%B5%9B_%E7%AB%9E%E8%B5%9B%E4%BF%A1%E6%81%AF.html
[AI_- 3]: https://www.pkbigdata.com/common/cmpt/AI%E5%BC%80%E5%8F%91%E8%80%85%E5%A4%A7%E8%B5%9B-%E9%98%BF%E5%B0%94%E8%8C%A8%E6%B5%B7%E9%BB%98%E7%BB%BC%E5%90%88%E5%BE%81%E9%A2%84%E6%B5%8B%E6%8C%91%E6%88%98%E8%B5%9B_%E7%AB%9E%E8%B5%9B%E4%BF%A1%E6%81%AF.html
[https_autodl.lri.fr_competitions_146]: https://autodl.lri.fr/competitions/146
[AutoDL]: https://autodl.chalearn.org/
[NeurIPS 2019 1]: https://neurips.cc/Conferences/2019/CallForCompetitions
[http_challenge.xfyun.cn_2019]: http://challenge.xfyun.cn/2019/
[https_www.kaggle.com_c_siim-acr-pneumothorax-segmentation]: https://www.kaggle.com/c/siim-acr-pneumothorax-segmentation
[C-MIMI 2019]: https://siim.org/page/2019cmimi
[https_www.kaggle.com_c_champs-scalar-coupling]: https://www.kaggle.com/c/champs-scalar-coupling
[https_www.kaggle.com_c_aptos2019-blindness-detection]: https://www.kaggle.com/c/aptos2019-blindness-detection
[4th APTOS Symposium]: http://2019.asiateleophth.org/
[https_qmul-survface.github.io]: https://qmul-survface.github.io/
[EvalAI 1]: https://evalai.cloudcv.org/web/challenges/challenge-page/392/
[https_www.biendata.com_competition_datagrand]: https://www.biendata.com/competition/datagrand/
[http_challenge.ai.iqiyi.com_detail_raceId_5b112a742a360316a898ff50]: http://challenge.ai.iqiyi.com/detail?raceId=5b112a742a360316a898ff50
[iQIYI]: http://challenge.ai.iqiyi.com/
[AVS Workgroup]: http://www.avs.org.cn/english/index.asp
[http_mrp.nlpl.eu]: http://mrp.nlpl.eu/
[CodaLab 5]: https://competitions.codalab.org/competitions/23524
[https_youtube-vos.org_challenge_2019]: https://youtube-vos.org/challenge/2019/
[Track 1_ Video Object Segmentation]: https://youtube-vos.org/dataset/vos/
[Track 2_ Video Instance Segmentation]: https://youtube-vos.org/dataset/vis/
[https_www.kaggle.com_c_youtube8m-2019]: https://www.kaggle.com/c/youtube8m-2019
[https_structseg2019.grand-challenge.org]: https://structseg2019.grand-challenge.org/
[MICCAI 2019]: https://www.miccai2019.org/
[https_research.fb.com_programs_openeds-challenge]: https://research.fb.com/programs/openeds-challenge
[EvalAI 2]: https://evalai.cloudcv.org/
[Facebook]: https://research.fb.com/
[Track-1 Semantic Segmentation challenge]: https://evalai.cloudcv.org/web/challenges/challenge-page/353
[Track-2 Synthetic Eye Generation challenge]: https://evalai.cloudcv.org/web/challenges/challenge-page/354
[https_exoplanet-imaging-challenge.github.io]: https://exoplanet-imaging-challenge.github.io/
[CodaLab 6]: https://competitions.codalab.org/competitions/22836
[https_www.biendata.com_competition_idiom]: https://www.biendata.com/competition/idiom/
[https_odir2019.grand-challenge.org]: https://odir2019.grand-challenge.org/
[Grand Challenges]: https://patchcamelyon.grand-challenge.org/
[Link 18]: https://www.pku.edu.cn/
[https_digestpath2019.grand-challenge.org]: https://digestpath2019.grand-challenge.org/
[http_qfc-c.com]: http://qfc-c.com/
[Link 19]: http://www.hengqin.gov.cn/hq/index.html
[https_storage.googleapis.com_openimages_web_challenge2019.html]: https://storage.googleapis.com/openimages/web/challenge2019.html
[Open Images V5]: https://g.co/dataset/openimages
[Object detection]: https://www.kaggle.com/c/open-images-2019-object-detection
[Visual relationship detection track]: https://www.kaggle.com/c/open-images-2019-visual-relationship
[http_ai.bu.edu_visda-2019]: http://ai.bu.edu/visda-2019/
[ICCV 2019 1]: https://eccv2018.org/
[Multi-Source Domain Adaptation Challenge]: https://competitions.codalab.org/competitions/22469
[Semi-Supervised Domain Adaptation]: https://competitions.codalab.org/competitions/22470
[https_www.dcjingsai.com_common_cmpt_AI in RTC-_.html]: https://www.dcjingsai.com/common/cmpt/AI%20in%20RTC-%E8%B6%85%E5%88%86%E8%BE%A8%E7%8E%87%E5%9B%BE%E5%83%8F%E8%B4%A8%E9%87%8F%E6%AF%94%E8%BE%83%E6%8C%91%E6%88%98%E8%B5%9B_%E7%AB%9E%E8%B5%9B%E4%BF%A1%E6%81%AF.html
[https_www.dcjingsai.com_common_cmpt_AI in RTC-_.html 1]: https://www.dcjingsai.com/common/cmpt/AI%20in%20RTC-%E8%B6%85%E5%88%86%E8%BE%A8%E7%8E%87%E7%AE%97%E6%B3%95%E6%80%A7%E8%83%BD%E6%AF%94%E8%BE%83%E6%8C%91%E6%88%98%E8%B5%9B_%E7%AB%9E%E8%B5%9B%E4%BF%A1%E6%81%AF.html
[https_alchemy.tencent.com]: https://alchemy.tencent.com/
[CodaLab 7]: https://competitions.codalab.org/competitions/22974
[Tencent Quantum Lab]: https://www.tencent.com/en-us/
[GDBMedChem]: http://gdb.unibe.ch/downloads/
[PySCF]: https://github.com/pyscf/pyscf
[https_competitions.codalab.org_competitions_23391]: https://competitions.codalab.org/competitions/23391
[ICCV 2019 2]: https://sites.google.com/view/lingir
[Github]: https://github.com/XiaoxiaoGuo/fashion-iq
[https_micronet-challenge.github.io]: https://micronet-challenge.github.io/
[NeurIPS 2019 2]: https://nips.cc/
[ImageNet Classification]: http://image-net.org/index
[CIFAR-100 Classification]: https://www.cs.toronto.edu/~kriz/cifar.html
[WikiText-103 Language Modeling]: https://blog.einstein.ai/the-wikitext-long-term-dependency-language-modeling-dataset/
[https_www.microsoft.com_en-us_research_project_multi-domain-task-completion-dialog-challenge]: https://www.microsoft.com/en-us/research/project/multi-domain-task-completion-dialog-challenge/
[DSTC8]: https://sites.google.com/dstc.community/dstc8/home?authuser=0
[end-to-end multi-domain]: https://competitions.codalab.org/competitions/23263
[https_endovis.grand-challenge.org_Endoscopic_Vision_Challenge]: https://endovis.grand-challenge.org/Endoscopic_Vision_Challenge/
[Surgical Workflow and Skill Analysis]: https://endovissub-workflowandskill.grand-challenge.org/
[Stereo Correspondence and Reconstruction of Endoscopic Data]: https://endovissub2019-scared.grand-challenge.org/
[http_research.nii.ac.jp_graphgolf]: http://research.nii.ac.jp/graphgolf/
[CodaLab 8]: https://competitions.codalab.org/competitions/22858
[CANDAR 2019]: https://is-candar.org/
[http_animalaiolympics.com]: http://animalaiolympics.com/
[https_competitions.codalab.org_competitions_20115]: https://competitions.codalab.org/competitions/20115
[EPIC-KITCHENS 2018]: https://epic-kitchens.github.io/2018
[Action-Anticipation Challenge]: https://competitions.codalab.org/competitions/20071
[Object-Detection Challenge]: https://competitions.codalab.org/competitions/20111
[https_www.herox.com_IARPAGFChallenge2]: https://www.herox.com/IARPAGFChallenge2
[Herox]: https://www.herox.com/
[https_evalai.cloudcv.org_web_challenges_challenge-page_151]: https://evalai.cloudcv.org/web/challenges/challenge-page/151
[https_www.kesci.com_home_competition_5c234c6626ba91002bfdfdd3]: https://www.kesci.com/home/competition/5c234c6626ba91002bfdfdd3
[http_spiechallenges.cloudapp.net_competitions_14]: http://spiechallenges.cloudapp.net/competitions/14
[Grand Challenges 1]: https://grand-challenge.org/challenges/
[cloudapp.net]: http://spiechallenges.cloudapp.net/competitions/
[https_challengedata.ens.fr_challenges_15]: https://challengedata.ens.fr/challenges/15
[Challenge data]: https://challengedata.ens.fr/
[Oze Energies]: https://www.optimzen.com/
[https_challengedata.ens.fr_challenges_17]: https://challengedata.ens.fr/challenges/17
[Posos]: https://www.posos.fr/
[https_challengedata.ens.fr_challenges_18]: https://challengedata.ens.fr/challenges/18
[OWKIN]: https://owkin.com/
[https_challengedata.ens.fr_challenges_19]: https://challengedata.ens.fr/challenges/19
[Generali]: https://institutionnel.generali.fr/liste-des-offres
[https_challengedata.ens.fr_challenges_14]: https://challengedata.ens.fr/challenges/14
[GNT ENS]: https://lnc2.dec.ens.fr/en
[https_challengedata.ens.fr_challenges_13]: https://challengedata.ens.fr/challenges/13
[Napoleon X]: https://www.napoleonx.ai/
[https_challengedata.ens.fr_challenges_12]: https://challengedata.ens.fr/challenges/12
[BCM Energy]: https://www.planete-oui.fr/particuliers
[https_challengedata.ens.fr_challenges_10]: https://challengedata.ens.fr/challenges/10
[Dreem]: https://dreem.com/
[https_challengedata.ens.fr_challenges_7]: https://challengedata.ens.fr/challenges/7
[Plume Labs]: https://plumelabs.com/en/
[https_challengedata.ens.fr_challenges_6]: https://challengedata.ens.fr/challenges/6
[Enedis]: https://www.enedis.fr/
[Enedis_ Half hourly Electrical Balancing]: https://data.enedis.fr/explore/dataset/bilan-electrique-demi-heure/information/
[https_challengedata.ens.fr_challenges_20]: https://challengedata.ens.fr/challenges/20
[LumenAI]: http://www.lumenai.fr/
[https_challengedata.ens.fr_challenges_9]: https://challengedata.ens.fr/challenges/9
[Natixis]: http://www.natixis.com/
[https_challengedata.ens.fr_challenges_11]: https://challengedata.ens.fr/challenges/11
[Mauna Kea Technologies]: https://www.maunakeatech.com/
[https_challengedata.ens.fr_challenges_16]: https://challengedata.ens.fr/challenges/16
[CFM]: http://cfm.fr/
[https_my.vanderbilt.edu_memento]: https://my.vanderbilt.edu/memento/
[sub-challenge _1]: https://my.vanderbilt.edu/memento/sample-page/
[sub-challenge _2]: https://my.vanderbilt.edu/memento/sub-challenge-2/
[sub-challenge _3]: https://my.vanderbilt.edu/memento/sub-challenge-3/
[https_www.crowdanalytix.com_contests_propensity-to-fund-mortgages]: https://www.crowdanalytix.com/contests/propensity-to-fund-mortgages
[CrowdANALYTIX]: https://www.crowdanalytix.com/
[https_www.crowdanalytix.com_contests_identify-characters-from-product-images]: https://www.crowdanalytix.com/contests/identify-characters-from-product-images
[https_kits19.grand-challenge.org]: https://kits19.grand-challenge.org/
[https_paip2019.grand-challenge.org]: https://paip2019.grand-challenge.org/
[https_www.kaggle.com_c_imagenet-object-localization-challenge]: https://www.kaggle.com/c/imagenet-object-localization-challenge
[https_nocaps.org]: https://nocaps.org/
[EvalAI 3]: https://evalai.cloudcv.org/web/challenges/challenge-page/355/
[https_www.nuscenes.org]: https://www.nuscenes.org/
[EvalAI 4]: https://evalai.cloudcv.org/web/challenges/challenge-page/356/
[CVPR 2019]: http://cvpr2019.thecvf.com/
[https_www.kaggle.com_c_competitive-data-science-predict-future-sales]: https://www.kaggle.com/c/competitive-data-science-predict-future-sales
[https_competitions.codalab.org_competitions_15475]: https://competitions.codalab.org/competitions/15475
[https_competitions.codalab.org_competitions_23563]: https://competitions.codalab.org/competitions/23563
[http_youcook2.eecs.umich.edu]: http://youcook2.eecs.umich.edu/
[CodaLab 9]: https://competitions.codalab.org/competitions/23510
[Github 1]: https://github.com/MichiganCOG/Video-Grounding-from-Text
[https_competitions.codalab.org_competitions_23386]: https://competitions.codalab.org/competitions/23386
[CVPPP 2019]: https://www.plant-phenotyping.org/CVPPP2019
[https_competitions.codalab.org_competitions_23245]: https://competitions.codalab.org/competitions/23245
[https_competitions.codalab.org_competitions_22946]: https://competitions.codalab.org/competitions/22946
[https_fastballs.wordpress.com_category_pitchfx-glossary]: https://fastballs.wordpress.com/category/pitchfx-glossary/
[CodaLab 10]: https://competitions.codalab.org/competitions/22953
[https_github.com_facebookresearch_ActivityNet-Entities]: https://github.com/facebookresearch/ActivityNet-Entities
[CodaLab 11]: https://competitions.codalab.org/competitions/22835
[repo]: https://github.com/facebookresearch/grounded-video-description
[paper]: https://arxiv.org/pdf/1812.06587.pdf
[CodaLab 12]: https://competitions.codalab.org/competitions/21492
[https_competitions.codalab.org_competitions_20940]: https://competitions.codalab.org/competitions/20940
[TextWorld]: https://github.com/Microsoft/TextWorld
[https_competitions.codalab.org_competitions_20686]: https://competitions.codalab.org/competitions/20686
[TVQA]: http://tvqa.cs.unc.edu/index.html
[https_competitions.codalab.org_competitions_20220]: https://competitions.codalab.org/competitions/20220
[IWCS-2019]: https://sites.google.com/view/iwcs2019
[https_competitions.codalab.org_competitions_20574]: https://competitions.codalab.org/competitions/20574
[IntPhys]: http://www.intphys.com/challenge.html
[http_alt.qcri.org_semeval2019_index.php_id_tasks]: http://alt.qcri.org/semeval2019/index.php?id=tasks
[SemEval-2019]: http://alt.qcri.org/semeval2019/
[Task 1_ Cross-lingual Semantic Parsing with UCCA]: https://competitions.codalab.org/competitions/19160
[Task 2_ Unsupervised Lexical Semantic Frame Induction]: https://competitions.codalab.org/competitions/19159
[Task 3_ EmoContext_ Contextual Emotion Detection in Text]: https://www.humanizing-ai.com/emocontext.html
[Task 4_ Hyperpartisan News Detection]: http://www.webis.de/events/semeval-19
[Task 5_ HatEval_ Multilingual Detection of Hate Speech Against Immigrants and Women in Twitter]: https://competitions.codalab.org/competitions/19935
[Task 6_ OffensEval_ Identifying and Categorizing Offensive Language in Social Media_a_ _Fact vs fiction_]: https://competitions.codalab.org/competitions/20011
[Task 7_ RumourEval 2019_ Determining Rumour Veracity and Support for Rumours]: https://competitions.codalab.org/competitions/19938
[Task 8_ Fact Checking in Community Question Answering Forums]: https://competitions.codalab.org/competitions/20022
[Task 9_ Suggestion Mining from Online Reviews and Forums]: https://competitions.codalab.org/competitions/19955
[Task 10_ Math Question Answering]: https://competitions.codalab.org/competitions/20013
[Task 12_ Toponym Resolution in Scientific Papers]: https://competitions.codalab.org/competitions/19948
[https_competitions.codalab.org_competitions_20010]: https://competitions.codalab.org/competitions/20010
[https_pilehvar.github.io_wic]: https://pilehvar.github.io/wic
[https_competitions.codalab.org_competitions_19529]: https://competitions.codalab.org/competitions/19529
[ECCV 2018]: https://oxuva.github.io/long-term-tracking-benchmark/
[https_unearthed.solutions_u_competitions_evergreen_get-2-the-core]: https://unearthed.solutions/u/competitions/evergreen/get-2-the-core
[Unearthed]: https://unearthed.solutions/
[https_unearthed.solutions_u_competitions_evergreen_get-2-core-ii-revenge-depths]: https://unearthed.solutions/u/competitions/evergreen/get-2-core-ii-revenge-depths
[https_unearthed.solutions_u_competitions_evergreen_hydrosaver]: https://unearthed.solutions/u/competitions/evergreen/hydrosaver
[https_lyon19.grand-challenge.org]: https://lyon19.grand-challenge.org/
[https_drive.grand-challenge.org]: https://drive.grand-challenge.org/
[https_github.com_basveeling_pcam]: https://github.com/basveeling/pcam
[https_verse2019.grand-challenge.org]: https://verse2019.grand-challenge.org/
[https_evalai.cloudcv.org_web_challenges_challenge-page_97]: https://evalai.cloudcv.org/web/challenges/challenge-page/97
[http_vizwiz.org_data_challenge]: http://vizwiz.org/data/#challenge
[EvalAI 5]: https://evalai.cloudcv.org/web/challenges/challenge-page/102
[https_rajpurkar.github.io_SQuAD-explorer]: https://rajpurkar.github.io/SQuAD-explorer/
[Stanford NLP Group]: https://nlp.stanford.edu/blog/
[https_stanfordnlp.github.io_coqa]: https://stanfordnlp.github.io/coqa/
[coca]: https://en.wikipedia.org/wiki/Coca
[https_github.com_google_unrestricted-adversarial-examples]: https://github.com/google/unrestricted-adversarial-examples
[Google AI]: https://ai.googleblog.com/2018/09/introducing-unrestricted-adversarial.html
[http_decanlp.com]: http://decanlp.com/
[salesforce]: https://github.com/salesforce