人工智能/数据科学比赛汇总 2019.6

怼烎@ 2022-01-13 11:27 7339阅读 0赞

## **2019世界机器人大赛—BCI脑控机器人大赛 暨 第三届中国脑机接口比赛** ##

[http://www.worldrobotconference.com/html/jiqirendasai/saishi/2018/0314/290.html][http_www.worldrobotconference.com_html_jiqirendasai_saishi_2018_0314_290.html]

5月9日-7月9日, 2019 // Host by [DataFountain][] // Prize: ￥100,000

**Note**: 脑-机接口（BCI）是指通过对神经系统电活动和特征信号的收集、识别及转化，使人脑发出的指令能够直接传递给指定的机器终端，从而使人对机器人的控制和操作更为高效便捷，该项技术在人与机器人的交流沟通领域有着重大创新意义和使用价值，其已广泛应用于助残康复、灾害救援、娱乐体验等多个领域，并在改善残疾人生活质量中做出了巨大贡献。

Entry Deadline:

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## **Game of Deep Learning: Computer Vision Hackathon** ##

[https://datahack.analyticsvidhya.com/contest/game-of-deep-learning/][https_datahack.analyticsvidhya.com_contest_game-of-deep-learning]

May 25 - Jun 10, 2019 // Host by [Analytics Vidhya][] // Prize: Datahack Summit 2019 Tickets & Job Opportunities with AV

**Note**: Do you feel passionate about solving problems through deep learning?  
Do you aspire to take data science to millions of people out there? Can the leader in you make people follow data science out of sheer passion?  
If the answer to all the questions is yes – look no more. Analytics Vidhya is looking for evangelists who can carry and deliver their baton to the world.

Entry Deadline:

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## **Urban Region Function Classification** ##

[https://dianshi.baidu.com/competition/30/rule][https_dianshi.baidu.com_competition_30_rule]

April 30th - September, 2019 // Host by [点石][Link 5] // Prize: RMB150,000

**Note**: 2019 The 5th Baidu & XJTU Big Data Competition And The First IKCEST “One Belt One Road” International Big Data Competition.  
Build models to classify the functions of urban areas with data of satellite images and user behavior from given geographical areas.

Entry Deadline:

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## **Science of Science 数据黑客松** ##

[https://www.biendata.com/competition/hackathon/][https_www.biendata.com_competition_hackathon]

06/18 - 06/12 2019 // Host by [Biendata][] // Prize: 40,000元

**Note**: 科学论文虽然代表了人类对自然最前沿的理解，但大众却很难读懂论文。所以论文发表后，科研机构经常会发布研究新闻稿解释论文中的研究。本次比赛的任务是给定一篇科研新闻，找到这篇新闻描述的论文。  
提交数据格式: 新闻数据测试集press\_test.csv中的每篇新闻都是在报道论文测试集paper\_test.csv中的一篇论文。对于每篇新闻，选手需要从paper\_test.csv中选出三篇最相关的论文。参赛者需要在训练集上训练模型，并对新闻和论文的相关行进行预测，最终可以标注 Top 3 的匹配结果。注意：预测出的匹配论文之间存在顺序，按照预测得分，从大到小排序。  
本次黑客松任务即挂靠这次会议。在4天的时间里，参赛选手需要通过平台提交预测结果。前三名选手将获得4万元奖金，并被邀请到会议上介绍获奖方法。

Entry Deadline:

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## **Objects 365 图片物体检测** ##

[https://www.biendata.com/competition/objects365/][https_www.biendata.com_competition_objects365]

04/29 - 06/10 2019 // Host by [Biendata][] & [CVPR 2019][] // Prize: $20,000

**Note**: Objects365是一个全新的数据集，旨在促进对自然场景不同对象的检测研究。Objects365在638K张图像上标注了365个对象类，训练集中共有超过1000万个边界框。因此，这些标注涵盖了发生在各种场景类别中的常见对象。

Entry Deadline:

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## **Crowd Human 人体检测** ##

[https://www.biendata.com/competition/crowdhuman/][https_www.biendata.com_competition_crowdhuman]

04/29 - 06/10 2019 // Host by [Biendata][] & [CVPR 2019][] // Prize: $10,000

**Note**: CrowdHuman是目前最大的人体检测数据集，带有充分的标注信息，图片来源具有丰富的多样性。包含15000张训练集，4370张验证集和5000张测试集，在数据集中，总共有340K个人体目标，平均每张图片有22.6个人，数据集中有各种各样的遮挡场景。每个人的实例都有一个头部边界框、可见区域边界框和全身边界框。

Entry Deadline:

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## **2019中国高校计算机大赛——大数据挑战赛** ##

[https://www.kesci.com/home/competition/5cb80fd312c371002b12355f][https_www.kesci.com_home_competition_5cb80fd312c371002b12355f]

2019-04-28 至 2019-06-25 // Host by [Kesci][] // Prize: 30万元人民币

**Note**: 2019大数据挑战赛（以下简称“大赛”）是在中国高校计算机大赛主办单位的指导下，由清华大学、南开大学与字节跳动公司联合主办，亚马逊AWS提供资源支持以及科赛提供竞赛平台支持，并以企业真实场景和实际数据为基础的高端算法竞赛。大赛面向全球高校在校生开放，旨在提升高校学生对数据分析与处理的算法研究与技术应用能力，探索大数据的核心科学与技术问题，尝试创新大数据技术，推动大数据的产学研用，本次大赛鼓励高校教师参与指导。  
预选赛——文本情感分类模型  
正式赛题——文本点击率预估（5月26日开赛）

Entry Deadline:

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## **2019 EE331 - Colorization** ##

[https://competitions.codalab.org/competitions/23046][https_competitions.codalab.org_competitions_23046]

May 27 - June 15, 2019 // Host by [CodaLab][https_competitions.codalab.org_competitions_23046] // Prize: NaN

**Note**: Our goal is to make colorization from input images.  
The normal images are composed of RGB values, but we can represent these images into LAB values.  
In here, L means lightness.  
We cannot know the color of the image without the A and B values.

Entry Deadline:

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## **阿里巴巴优酷视频增强和超分辨率挑战赛** ##

[https://tianchi.aliyun.com/competition/entrance/231711/introduction?spm=5176.12281949.1003.1.493e4c2aaxG9Ek][https_tianchi.aliyun.com_competition_entrance_231711_introduction_spm_5176.12281949.1003.1.493e4c2aaxG9Ek]

April 11 - September, 2019 // Host by [天池][Link 6] & [YOUKU][] // Prize: ￥230000

**Note**: 比赛要求通过训练样本对视频增强和超分模型进行建模，对测试集中的低分辨率视频样本预测高分辨率视频。其中，高分辨率视频来自优酷高清媒资库，优酷拥有视频的知识产权。低分辨率视频的生成模型是模拟实际业务中的噪声模式。

Entry Deadline:

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## **2019 AIWIN 5G环境下无人驾驶挑战赛 / Self-driving Car Challenge under 5G Environment** ##

[https://www.kesci.com/home/competition/5cbd8083c7d626002beb280d][https_www.kesci.com_home_competition_5cbd8083c7d626002beb280d]

2019-04-25 至 2019-07-31 // Host by [Kesci][] // Prize: ¥ 200,000 + SAIL奖推荐

**Note**: Under 5G network environment, Self-driving car faces new opportunities and challenges. International teams are invited to join us at LinGang’s Self-driving car closed test zone to conduct real road test and virtual scene test.  
1、无人驾驶 5G应用挑战 / Self-driving Car 5G application challenge  
2、自动驾驶算法虚拟场景挑战 / Automatic driving algorithm Virtual scene Challenge

Entry Deadline:

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## **Jigsaw Unintended Bias in Toxicity Classification** ##

[https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification][https_www.kaggle.com_c_jigsaw-unintended-bias-in-toxicity-classification]

Now - July 10, 2019 // Host by [Kaggle][] // Prize: $65,000

**Note**: This is a Kernels-only competition.  
Detect toxicity across a diverse range of conversations

Entry Deadline:

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## **Learning to Run a Power Network (L2RPN 2019)** ##

[https://l2rpn.chalearn.org][https_l2rpn.chalearn.org]

May 15th - Jul 14th, 2019 // Host by [CodaLab][] & [IJCNN 2019][] // Prize: 3000$ travel expenses

**Note**: The challenge is based on an environment (and not solely a dataset) in which an agent can learn by interactions.

Entry Deadline:

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## **The 1st Chinese Audio-Textual Spoken Language Understanding Challenge (CATSLU)** ##

[https://sites.google.com/view/CATSLU/home][https_sites.google.com_view_CATSLU_home]

15/03 - 12/08, 2019 // Host by [ICMI 2019][] // Prize: NaN

**Note**: The spoken language understanding (SLU) is a key component of spoken dialogue system (SDS), parsing user’s utterances into corresponding semantic concepts. To fully investigate these problems and promote application of spoken dialogue system, we will release a multi-turn task-oriented Chinese spoken dialog dataset and organize the first open, audio-text based Chinese Task Oriented Spoken Language Understanding Challenge. This challenge consists of two sub-challenges:  
SLU in domain  
Domain adaptation of SLU

Entry Deadline:

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## **Instant Gratification** ##

[https://www.kaggle.com/c/instant-gratification][https_www.kaggle.com_c_instant-gratification]

Now - June 20, 2019 // Host by [Kaggle][] // Prize: $5,000

**Note**: A synchronous Kernels-only competition

Entry Deadline:

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## **Data Science for Good: City of Los Angeles** ##

[https://www.kaggle.com/c/data-science-for-good-city-of-los-angeles][https_www.kaggle.com_c_data-science-for-good-city-of-los-angeles]

May 8th - June 21, 2019 // Host by [Kaggle][] // Prize: $15,000

**Note**: Help the City of Los Angeles to structure and analyze its job descriptions.  
The goal is to convert a folder full of plain-text job postings into a single structured CSV file and then to use this data to: (1) identify language that can negatively bias the pool of applicants; (2) improve the diversity and quality of the applicant pool; and/or (3) make it easier to determine which promotions are available to employees in each job class.

Entry Deadline:

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## **Virtual Creatures Competition** ##

[https://virtualcreatures.github.io][https_virtualcreatures.github.io]

Now - June 23, 2019 // Host by [GECCO 2019][] // Prize: NaN

**Note**: The purpose of the contest is not to build a system to solve a task better than any other system; rather, the goal is to engage our imagination and creativity—to expand our conception of life and intelligence, and how these things might materialize.

Entry Deadline:

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## **The 1st Mandarin Audio-Visual Speech Recognition Challenge (MAVSR)** ##

[http://vipl.ict.ac.cn/homepage/mavsr/index.html][http_vipl.ict.ac.cn_homepage_mavsr_index.html]

April 11 - August 8, 2019 // Host by [ICMI 2019][] // Prize: ~30,000 RMB

**Note**: This challenge aims at exploring the complementarity between visual and acoustic information in real-world speech recognition systems, and will be held at ACM International Conference on Multimodal Interaction (ICMI) 2019.  
Sub-challenge 1: Closed-set word-level speech recognition  
Sub-challenge 2: Open-set word-level speech recognition  
Sub-challenge 3: Visual keyword spotting

Entry Deadline:

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## **PharmaCoNER: Pharmacological Substances, Compounds and proteins and Named Entity Recognition track** ##

[http://temu.bsc.es/pharmaconer/][http_temu.bsc.es_pharmaconer]

March 18 - November 3, 2019 // Host by [CodaLab][CodaLab 1] // Prize: 1,700€x2

**Note**: For this task we have prepared a manually classified collection of clinical case sections derived from Open access Spanish medical publications, named the Spanish Clinical Case Corpus (SPACCC). The corpus contains a total of 1000 clinical cases / 396,988 words. It is noteworthy to say that this kind of narrative shows properties of both, the biomedical and medical literature as well as clinical records.  
Track 1: NER offset and entity classification.  
Track 2: Concept indexing.

Entry Deadline:

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## **政府治理大数据应用算法大赛** ##

[http://campus.chinahr.com/pages/cetctwo/][http_campus.chinahr.com_pages_cetctwo]

2019-05-27 至 2019-08-31 // Host by [中电科大数据研究院有限公司][Link 7] // Prize: ￥14万

**Note**: 国内外全日制**在校**大学生（包括全日制本科、硕士、博士研究生等非2019届毕业生），热爱算法建模、热爱数据分析、热爱应用开发等均可参赛。  
本轮赛事仅设1题: **政策公文中的语义搜索**(需要根据输入的问题，返回对应的政策内容，以及相应的公文名称和具体条例)

Entry Deadline:

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## **转子部件脱落故障预测 (Prognosis of Rotor Parts Fly-off)** ##

[http://www.industrial-bigdata.com/competition/competitionAction!showDetail2019.action?competition.competitionId=5][http_www.industrial-bigdata.com_competition_competitionAction_showDetail2019.action_competition.competitionId_5]

5月24日 - 7月22日, 2019 // Host by [中国信息通信研究院][Link 8] // Prize: ￥200,000

**Note**: **第三届工业大数据创新竞赛**  
Background: For large rotating machines, due to the high rotating speed and tremendous momentum of the rotor, the centrifugal force may lead to the loose or flying apart of the rotor parts, which brings a great threat to the operation safety of the equipment. This type of fault could lead to high maintenance cost and significant economic loss. The capture of the early symptom of rotor faults is relatively difficult and has become a worldwide challenge in the field of prognostics. It will bring significant safety value and economic benefits if the big data-based methods can detect and predict incipient faults.  
赛事简要：大型旋转机组设备高速旋转、动能巨大，转子部件脱落故障给设备安全运行带来巨大威胁。而且这类故障维修成本高、耗时长，早期征兆难以捕捉，是故障预测的世界性难题。如果能通过大数据方法，实现此故障征兆的早期发现和程度预测，将带来显著的安全价值和经济效益。

Entry Deadline:

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## **RecSys Challenge 2019** ##

[http://www.recsyschallenge.com/2019/][http_www.recsyschallenge.com_2019]

March - September, 2019 // Host by [ACM RecSys conference][] // Prize: NaN

**Note**: This year’s challenge is to develop a session-based and context-aware recommender system to adapt a list of accommodations according to the needs of the user.

Entry Deadline:

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## **2019中国‘法研杯’司法人工智能挑战赛（CAIL2019）** ##

[http://cail.cipsc.org.cn][http_cail.cipsc.org.cn]

5月15日 - 7月26日, 2019 // Host by [中国中文信息学会 (CIPS)][_CIPS] // Prize: 60,000RMB

**Note**: 本次挑战赛聚焦在**法律要素抽取**、**法律阅读理解**、**相似案例匹配**三个真实场景的任务，提供海量的已标注的法律文书数据，旨在为研究者提供学术交流平台，推动语言理解和人工智能领域技术在法律领域的应用，促进法律人工智能事业的发展。今年预计于10月在北京举办颁奖会和技术研讨会。诚邀学术界和工业界的研究者和开发者积极参与，比赛中发现的重大基础性问题，在国家自然科学基金专项资金中将予以重点考虑！

Entry Deadline:

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## **CMRC 2019: The Third Evaluation Workshop on Chinese Machine Reading Comprehension** ##

[https://hfl-rc.github.io/cmrc2019/][https_hfl-rc.github.io_cmrc2019]

May 23th - Oct 18th, 2019 // Host by [CodaLab][CodaLab 2] & [中国中文信息学会计算语言学专业委员会（CIPS-CL）][CIPS-CL] & [哈工大讯飞联合实验室（HFL）][HFL] & [科大讯飞股份有限公司][Link 9] // Prize: 35,000 CNY

**Note**: \*\*第三届“讯飞杯”中文机器阅读理解（CMRC 2019）\*\*的任务是句子级填空型阅读理解（Sentence Cloze-Style Machine Reading Comprehension, SC-MRC）。 根据给定的一个叙事篇章以及若干个从篇章中抽取出的句子，参赛者需要建立模型将候选句子精准的填回原篇章中，使之成为完整的一篇文章。本任务需要机器能够根据上下文的逻辑关系判断空缺部分的内容，进一步提升了机器阅读理解的难度。

Entry Deadline:

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## **Bi-Objective Optimisation for the Travelling Thief Problem** ##

[https://www.egr.msu.edu/coinlab/blankjul/gecco19-thief/][https_www.egr.msu.edu_coinlab_blankjul_gecco19-thief]

Now - June 30, 2019 // Host by [GECCO 2019][] // Prize: NaN

**Note**: The goal of this competition is to provide a platform for researchers in computational intelligence working on multi-component optimization problems.

Entry Deadline:

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## **Black Box Optimization Competition (BBComp)** ##

[https://bbcomp.ini.rub.de][https_bbcomp.ini.rub.de]

February 4 - June 30, 2019 // Host by [GECCO 2019][] // Prize: NaN

**Note**: The Black Box Optimization Competition is the first competition platform in the continuous domain where test problems are truly black boxes to participants. The only information known to optimizer and participant is the dimension of the problem, bounds on all variables, and a budget of black box queries. The competition covers single- and multi-objective optimization.

Entry Deadline:

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## **Internet of Things: Online Event Detection for Drinking Water Quality Control** ##

[http://www.spotseven.de/gecco/gecco-challenge/][http_www.spotseven.de_gecco_gecco-challenge]

End of January/Start of February - June 30, 2019 // Host by [GECCO 2019][] // Prize: NaN

**Note**: The task of this years competition is to develop an anomaly detection algorithm for the water- and environmental data set. Early identification of anomalies in water quality data is a challenging task.

Entry Deadline:

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## **CCKS 2019 人物关系抽取** ##

[https://www.biendata.com/competition/ccks\_2019\_ipre/][https_www.biendata.com_competition_ccks_2019_ipre]

04/20 - 07/25 2019 // Host by [Biendata][] // Prize: ￥43,000

**Note**: 在本次任务中，我们重点关注人物之间的关系抽取研究，简称IPRE（Inter-Personal Relationship Extraction）。给定一组人物实体对和包含该实体对的句子，找出给定实体对在已知关系表中的关系。

Entry Deadline:

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## **Pose Estimation Challenge** ##

[https://kelvins.esa.int/satellite-pose-estimation-challenge/][https_kelvins.esa.int_satellite-pose-estimation-challenge]

Feb. 1, 2019 - July 1, 2019 // Host by [Kelvins][] // Prize: NaN

**Note**: **One camera, one image, one pose.**  
In this challenge, you are tasked to estimate the pose of the Tango spacecraft from its synthetic and real images captured using computer graphics and a robotic testbed, respectively.

Entry Deadline:

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## **Microsoft AI Challenge India 2018** ##

[https://competitions.codalab.org/competitions/20616][https_competitions.codalab.org_competitions_20616]

Nov. 19, 2018 - July 1, 2019 // Host by [CodaLab][CodaLab 3] // Prize: NaN

**Note**: The Microsoft AI Challenge India 2018 is over now. The page is active for learning purposes only. Problem statement:  
“Given a user query and candidate passages corresponding to each, the task is to mark the most relevant passage which contains the answer to the user query.”

Entry Deadline:

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## **SMP 2019 ETST 第二届文本溯源技术评测** ##

[https://www.biendata.com/competition/smpetst2019/][https_www.biendata.com_competition_smpetst2019]

05/20 - 07/08 2019 // Host by [Biendata][] // Prize: $19,000

**Note**: SMP 2019文本溯源评测由中国中文信息学会社会媒体处理专业委员会主办，黑龙江工程学院承办。本次技术评测以科研立项或成果创新型审查为应用背景，文本溯源的目标是判断一个文本的内容是否复制或改编于另外一个或者多个文本。文本溯源技术在学术诚信检测、搜索引擎优化等领域有广泛应用。

Entry Deadline:

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## **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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## **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][CodaLab 4] // 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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## **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 3] & [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 6] // 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:

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

## **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:

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

## **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:

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

## **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 5] & [ICCV 2019][] & [商汤][Link 10] & [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 ##

[http://wider-challenge.org/comingsoon.html][http_wider-challenge.org_comingsoon.html], aims at soliciting new approaches to advance the state-of- the-art in face detection.

## WIDER Pedestrian Detection ##

[https://competitions.codalab.org/competitions/22852][https_competitions.codalab.org_competitions_22852], has the goal of gathering effective and efficient approaches to address the problem of pedestrian detection in unconstrained environments.

## WIDER Cast Search by Portrait ##

[https://competitions.codalab.org/competitions/22833][https_competitions.codalab.org_competitions_22833], presents an exciting challenge of searching cast across hundreds of movies.

## WIDER Person Search by Language ##

[https://competitions.codalab.org/competitions/22864][https_competitions.codalab.org_competitions_22864], aims to seek new approaches to search person by natural language.

Entry Deadline:

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

## **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:

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

## **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:

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

## **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 6] // 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 ##

[https://competitions.codalab.org/competitions/23095][https_competitions.codalab.org_competitions_23095]

## Track 2 Clothes Retrieval ##

[https://competitions.codalab.org/competitions/23098][https_competitions.codalab.org_competitions_23098]

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 11] // 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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## **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:

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

## **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 6] // 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 3] & [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:

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

## **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 7] & [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:

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

## **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 ##

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

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

[https://ariel-datachallenge.azurewebsites.net/\#][https_ariel-datachallenge.azurewebsites.net 1] to create pipelines for faster, more effective processing of the raw data gathered by the mission will be launched in June.

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 12] // 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:

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## **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:

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

## **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:

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

## **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 5] & [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 ##

[https://youtube-vos.org/dataset/vos/][https_youtube-vos.org_dataset_vos]

## Track 2: Video Instance Segmentation ##

[https://youtube-vos.org/dataset/vis/][https_youtube-vos.org_dataset_vis]

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 1] & [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 ##

[https://evalai.cloudcv.org/web/challenges/challenge-page/353][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_evalai.cloudcv.org_web_challenges_challenge-page_354]

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 8] // 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:

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

## **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 13] // 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:

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

## **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 14] // 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:

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

## **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 5] & [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 ##

[https://competitions.codalab.org/competitions/22469][https_competitions.codalab.org_competitions_22469]

## Semi-Supervised Domain Adaptation ##

[https://competitions.codalab.org/competitions/22470][https_competitions.codalab.org_competitions_22470]

Entry Deadline:

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

## **Alchemy Contest** ##

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

5/22 - 9/30, 2019 // Host by [CodaLab][CodaLab 9] & [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:

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

## **MicroNet Challenge @NeurIPS 2019** ##

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

June 1, 2018 - Dec 13, 2019 // Host by [NeurIPS 2019][] // 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 ##

[http://image-net.org/index:][http_image-net.org_index] 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 ##

[https://www.cs.toronto.edu/~kriz/cifar.html:][https_www.cs.toronto.edu_kriz_cifar.html] 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 ##

[https://blog.einstein.ai/the-wikitext-long-term-dependency-language-modeling-dataset/:][https_blog.einstein.ai_the-wikitext-long-term-dependency-language-modeling-dataset] 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:

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

## **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 ##

[https://endovissub-workflowandskill.grand-challenge.org][https_endovissub-workflowandskill.grand-challenge.org]

## Stereo Correspondence and Reconstruction of Endoscopic Data ##

[https://endovissub2019-scared.grand-challenge.org][https_endovissub2019-scared.grand-challenge.org]

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 10] & [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][] // 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:

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

## **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 3] // Prize: NaN

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 5] & [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.

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

## **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 5] & [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 5] // 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 11] // 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 12] & [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 13] // 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 3] // 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 3] & [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 3] & [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 3] & [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 3] & [SemEval-2019][] // Prize: NaN

**Note**:  
**Frame semantics and semantic parsing:**

## Task 1: Cross-lingual Semantic Parsing with UCCA ##

[https://competitions.codalab.org/competitions/19160][https_competitions.codalab.org_competitions_19160]

## Task 2: Unsupervised Lexical Semantic Frame Induction ##

[https://competitions.codalab.org/competitions/19159][https_competitions.codalab.org_competitions_19159]  
**Opinion, emotion and abusive language detection**

## Task 3: EmoContext: Contextual Emotion Detection in Text ##

[https://www.humanizing-ai.com/emocontext.html][https_www.humanizing-ai.com_emocontext.html]

## Task 4: Hyperpartisan News Detection ##

[http://www.webis.de/events/semeval-19][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][https_competitions.codalab.org_competitions_19935]  
[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 ##

[https://competitions.codalab.org/competitions/20022][https_competitions.codalab.org_competitions_20022]  
**Information extraction and question answering**

## Task 9: Suggestion Mining from Online Reviews and Forums ##

[https://competitions.codalab.org/competitions/19955][https_competitions.codalab.org_competitions_19955]

## Task 10: Math Question Answering ##

[https://competitions.codalab.org/competitions/20013][https_competitions.codalab.org_competitions_20013]  
**NLP for scientific applications**

## Task 12: Toponym Resolution in Scientific Papers ##

[https://competitions.codalab.org/competitions/19948][https_competitions.codalab.org_competitions_19948]

No Deadline.

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

## **WiC\_competition** ##

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

Aug. 18, 2018 - Never // Host by [CodaLab][CodaLab 3] // Prize: NaN

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

No Deadline.

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

## **EPIC-Kitchens Action Anticipation** ##

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

Aug. 1, 2018 - Never // Host by [CodaLab][CodaLab 3] & [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][https_competitions.codalab.org_competitions_20115]

## Action-Anticipation Challenge ##

[https://competitions.codalab.org/competitions/20071][https_competitions.codalab.org_competitions_20071]

## Object-Detection Challenge ##

[https://competitions.codalab.org/competitions/20111][https_competitions.codalab.org_competitions_20111]

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 3] & [ECCV 2018][] // Prize: NaN

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

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
[http_www.worldrobotconference.com_html_jiqirendasai_saishi_2018_0314_290.html]: http://www.worldrobotconference.com/html/jiqirendasai/saishi/2018/0314/290.html
[DataFountain]: https://www.datafountain.cn/
[https_datahack.analyticsvidhya.com_contest_game-of-deep-learning]: https://datahack.analyticsvidhya.com/contest/game-of-deep-learning/
[Analytics Vidhya]: https://datahack.analyticsvidhya.com/contest/all/?
[https_dianshi.baidu.com_competition_30_rule]: https://dianshi.baidu.com/competition/30/rule
[Link 5]: https://dianshi.baidu.com/competition/
[https_www.biendata.com_competition_hackathon]: https://www.biendata.com/competition/hackathon/
[Biendata]: https://www.biendata.com/
[https_www.biendata.com_competition_objects365]: https://www.biendata.com/competition/objects365/
[CVPR 2019]: http://cvpr2019.thecvf.com
[https_www.biendata.com_competition_crowdhuman]: https://www.biendata.com/competition/crowdhuman/
[https_www.kesci.com_home_competition_5cb80fd312c371002b12355f]: https://www.kesci.com/home/competition/5cb80fd312c371002b12355f
[Kesci]: https://www.kesci.com/
[https_competitions.codalab.org_competitions_23046]: https://competitions.codalab.org/competitions/23046
[https_tianchi.aliyun.com_competition_entrance_231711_introduction_spm_5176.12281949.1003.1.493e4c2aaxG9Ek]: https://tianchi.aliyun.com/competition/entrance/231711/introduction?spm=5176.12281949.1003.1.493e4c2aaxG9Ek
[Link 6]: https://tianchi.aliyun.com/
[YOUKU]: https://www.youku.com
[https_www.kesci.com_home_competition_5cbd8083c7d626002beb280d]: https://www.kesci.com/home/competition/5cbd8083c7d626002beb280d
[https_www.kaggle.com_c_jigsaw-unintended-bias-in-toxicity-classification]: https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification
[Kaggle]: https://www.kaggle.com/
[https_l2rpn.chalearn.org]: https://l2rpn.chalearn.org
[CodaLab]: https://competitions.codalab.org/competitions/22845
[IJCNN 2019]: https://www.ijcnn.org
[https_sites.google.com_view_CATSLU_home]: https://sites.google.com/view/CATSLU/home
[ICMI 2019]: https://icmi.acm.org/2019/
[https_www.kaggle.com_c_instant-gratification]: https://www.kaggle.com/c/instant-gratification
[https_www.kaggle.com_c_data-science-for-good-city-of-los-angeles]: https://www.kaggle.com/c/data-science-for-good-city-of-los-angeles
[https_virtualcreatures.github.io]: https://virtualcreatures.github.io
[GECCO 2019]: https://gecco-2019.sigevo.org/index.html/HomePage
[http_vipl.ict.ac.cn_homepage_mavsr_index.html]: http://vipl.ict.ac.cn/homepage/mavsr/index.html
[http_temu.bsc.es_pharmaconer]: http://temu.bsc.es/pharmaconer/
[CodaLab 1]: https://competitions.codalab.org/competitions/23159
[http_campus.chinahr.com_pages_cetctwo]: http://campus.chinahr.com/pages/cetctwo/
[Link 7]: http://chrcmp.chinahr.com/pages/zdk2019/
[http_www.industrial-bigdata.com_competition_competitionAction_showDetail2019.action_competition.competitionId_5]: http://www.industrial-bigdata.com/competition/competitionAction!showDetail2019.action?competition.competitionId=5
[Link 8]: http://www.industrial-bigdata.com/
[http_www.recsyschallenge.com_2019]: http://www.recsyschallenge.com/2019/
[ACM RecSys conference]: https://recsys.acm.org/recsys19/
[http_cail.cipsc.org.cn]: http://cail.cipsc.org.cn
[_CIPS]: http://www.cipsc.org.cn
[https_hfl-rc.github.io_cmrc2019]: https://hfl-rc.github.io/cmrc2019/
[CodaLab 2]: https://competitions.codalab.org/competitions/23059
[CIPS-CL]: http://www.cips-cl.org
[HFL]: https://hfl-rc.github.io
[Link 9]: https://www.iflytek.com
[https_www.egr.msu.edu_coinlab_blankjul_gecco19-thief]: https://www.egr.msu.edu/coinlab/blankjul/gecco19-thief/
[https_bbcomp.ini.rub.de]: https://bbcomp.ini.rub.de
[http_www.spotseven.de_gecco_gecco-challenge]: http://www.spotseven.de/gecco/gecco-challenge/
[https_www.biendata.com_competition_ccks_2019_ipre]: https://www.biendata.com/competition/ccks_2019_ipre/
[https_kelvins.esa.int_satellite-pose-estimation-challenge]: https://kelvins.esa.int/satellite-pose-estimation-challenge/
[Kelvins]: https://kelvins.esa.int
[https_competitions.codalab.org_competitions_20616]: https://competitions.codalab.org/competitions/20616
[CodaLab 3]: https://competitions.codalab.org
[https_www.biendata.com_competition_smpetst2019]: https://www.biendata.com/competition/smpetst2019/
[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
[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 4]: https://competitions.codalab.org/competitions/21948
[https_sites.google.com_view_emotiw2019]: https://sites.google.com/view/emotiw2019
[https_competitions.codalab.org_competitions_21226]: https://competitions.codalab.org/competitions/21226
[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
[https_www.biendata.com_competition_ccks_2019_6]: https://www.biendata.com/competition/ccks_2019_6/
[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/
[http_wider-challenge.org_2019.html]: http://wider-challenge.org/2019.html
[CodaLab 5]: https://competitions.codalab.org/
[ICCV 2019]: http://iccv2019.thecvf.com
[Link 10]: https://www.sensetime.com
[Amazon]: https://aws.amazon.com/rekognition/
[First WIDER Challenge Workshop]: http://wider-challenge.org/2018.html
[http_wider-challenge.org_comingsoon.html]: http://wider-challenge.org/comingsoon.html
[https_competitions.codalab.org_competitions_22852]: https://competitions.codalab.org/competitions/22852
[https_competitions.codalab.org_competitions_22833]: https://competitions.codalab.org/competitions/22833
[https_competitions.codalab.org_competitions_22864]: https://competitions.codalab.org/competitions/22864
[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_sites.google.com_view_cvcreative_deepfashion2_authuser_0]: https://sites.google.com/view/cvcreative/deepfashion2?authuser=0
[CodaLab 6]: https://competitions.codalab.org/competitions
[github]: https://github.com/switchablenorms/DeepFashion2
[https_competitions.codalab.org_competitions_23095]: https://competitions.codalab.org/competitions/23095
[https_competitions.codalab.org_competitions_23098]: https://competitions.codalab.org/competitions/23098
[http_contest.cfdsj.cn_index_care]: http://contest.cfdsj.cn/index/care
[Link 11]: http://cfdsj.cn
[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_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 7]: https://competitions.codalab.org/competitions/22842
[Euro CSS 2019]: http://symposium.computationalsocialscience.eu/2019/
[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
[https_ariel-datachallenge.azurewebsites.net_ML]: https://ariel-datachallenge.azurewebsites.net/ML
[https_ariel-datachallenge.azurewebsites.net_retrieval]: https://ariel-datachallenge.azurewebsites.net/retrieval
[https_ariel-datachallenge.azurewebsites.net 1]: https://ariel-datachallenge.azurewebsites.net/#
[http_challenge.xfyun.cn_2019]: http://challenge.xfyun.cn/2019/
[Link 12]: https://www.xfyun.cn
[https_www.kaggle.com_c_champs-scalar-coupling]: https://www.kaggle.com/c/champs-scalar-coupling
[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
[https_youtube-vos.org_challenge_2019]: https://youtube-vos.org/challenge/2019/
[https_youtube-vos.org_dataset_vos]: https://youtube-vos.org/dataset/vos/
[https_youtube-vos.org_dataset_vis]: https://youtube-vos.org/dataset/vis/
[https_research.fb.com_programs_openeds-challenge]: https://research.fb.com/programs/openeds-challenge
[EvalAI 1]: https://evalai.cloudcv.org/
[Facebook]: https://research.fb.com
[https_evalai.cloudcv.org_web_challenges_challenge-page_353]: https://evalai.cloudcv.org/web/challenges/challenge-page/353
[https_evalai.cloudcv.org_web_challenges_challenge-page_354]: https://evalai.cloudcv.org/web/challenges/challenge-page/354
[https_exoplanet-imaging-challenge.github.io]: https://exoplanet-imaging-challenge.github.io
[CodaLab 8]: https://competitions.codalab.org/competitions/22836
[https_odir2019.grand-challenge.org]: https://odir2019.grand-challenge.org/
[Grand Challenges]: https://patchcamelyon.grand-challenge.org
[Link 13]: https://www.pku.edu.cn
[http_qfc-c.com]: http://qfc-c.com
[Link 14]: http://www.hengqin.gov.cn/hq/index.html
[http_ai.bu.edu_visda-2019]: http://ai.bu.edu/visda-2019/
[ICCV 2019 1]: https://eccv2018.org
[https_competitions.codalab.org_competitions_22469]: https://competitions.codalab.org/competitions/22469
[https_competitions.codalab.org_competitions_22470]: https://competitions.codalab.org/competitions/22470
[https_alchemy.tencent.com]: https://alchemy.tencent.com
[CodaLab 9]: 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_micronet-challenge.github.io]: https://micronet-challenge.github.io/
[NeurIPS 2019]: https://nips.cc
[http_image-net.org_index]: http://image-net.org/index:
[https_www.cs.toronto.edu_kriz_cifar.html]: https://www.cs.toronto.edu/~kriz/cifar.html:
[https_blog.einstein.ai_the-wikitext-long-term-dependency-language-modeling-dataset]: https://blog.einstein.ai/the-wikitext-long-term-dependency-language-modeling-dataset/:
[https_endovis.grand-challenge.org_Endoscopic_Vision_Challenge]: https://endovis.grand-challenge.org/Endoscopic_Vision_Challenge/
[https_endovissub-workflowandskill.grand-challenge.org]: https://endovissub-workflowandskill.grand-challenge.org
[https_endovissub2019-scared.grand-challenge.org]: https://endovissub2019-scared.grand-challenge.org
[http_research.nii.ac.jp_graphgolf]: http://research.nii.ac.jp/graphgolf/
[CodaLab 10]: https://competitions.codalab.org/competitions/22858
[CANDAR 2019]: https://is-candar.org
[http_animalaiolympics.com]: http://animalaiolympics.com/
[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
[EvalAI 2]: https://evalai.cloudcv.org
[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
[MICCAI 2019]: https://www.miccai2019.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/
[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_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 11]: https://competitions.codalab.org/competitions/22953
[https_github.com_facebookresearch_ActivityNet-Entities]: https://github.com/facebookresearch/ActivityNet-Entities
[CodaLab 12]: https://competitions.codalab.org/competitions/22835
[repo]: https://github.com/facebookresearch/grounded-video-description
[paper]: https://arxiv.org/pdf/1812.06587.pdf
[http_youcook2.eecs.umich.edu]: http://youcook2.eecs.umich.edu
[CodaLab 13]: 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/
[https_competitions.codalab.org_competitions_19160]: https://competitions.codalab.org/competitions/19160
[https_competitions.codalab.org_competitions_19159]: https://competitions.codalab.org/competitions/19159
[https_www.humanizing-ai.com_emocontext.html]: https://www.humanizing-ai.com/emocontext.html
[http_www.webis.de_events_semeval-19]: http://www.webis.de/events/semeval-19
[https_competitions.codalab.org_competitions_19935]: 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
[https_competitions.codalab.org_competitions_20022]: https://competitions.codalab.org/competitions/20022
[https_competitions.codalab.org_competitions_19955]: https://competitions.codalab.org/competitions/19955
[https_competitions.codalab.org_competitions_20013]: https://competitions.codalab.org/competitions/20013
[https_competitions.codalab.org_competitions_19948]: 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_20115]: https://competitions.codalab.org/competitions/20115
[EPIC-KITCHENS 2018]: https://epic-kitchens.github.io/2018
[https_competitions.codalab.org_competitions_20071]: https://competitions.codalab.org/competitions/20071
[https_competitions.codalab.org_competitions_20111]: https://competitions.codalab.org/competitions/20111
[https_competitions.codalab.org_competitions_19529]: https://competitions.codalab.org/competitions/19529
[ECCV 2018]: https://oxuva.github.io/long-term-tracking-benchmark/
[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