Data Science Competitions Deadlines
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
6月24 - 9月09, 2019 // Host by 天池 // 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。
2019-06-19 至 2019-09-07 // Host by 深圳市政府数据开放平台 & 华为 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
NeurIPS 2019: Disentanglement Challenge
June 28th - September 24th, 2019 // Host by crowdAI & NeurIPS 2019 // Prize: 10,000 EUR x 2
Note: Given the growing importance of the field and the potential societal impact in the medical domain or fair decision making, it is high time to bring disentanglement to the real-world:
Stage 1: Sim-to-real transfer learning - design representation learning algorithms on simulated data and transfer them to the real world.
Stage 2: Advancing disentangled representation learning to complicated physical objects.
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。
6月8日 - 9月, 2019 // Host by 睡前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: 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: 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。
May 27 - July 30, 2019 // Host by CodaLab // 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
Snake Species Identification Challenge
January 21 - July 31, 2019 // Host by crowdAI // Prize: 2 x travel grant
Note: In this challenge you will be provided with a dataset of RGB images of snakes, and their corresponding species (class). The goal is to train a classification model.
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
2019-05-15 至 2019-10-31 // Host by 长风大数据平台 // 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。
2019-06-14 至 2019-09-20 // Host by 上海电信 // 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。
7月1日 - 9月23日, 2019 // Host by Baidu AIstudio // Prize: ￥112,000
Note: 本次竞赛任务为目标检测，参赛者需要找出所给图像中所有感兴趣的目标，确定它们的位置和大小。参赛者需提供一个飞桨（PaddlePaddle）模型，模型输出所给图片中每个目标的信息，包括boundingbox（[x0,y0,x1,y1]）、类别信息和分数。
6月24 - 9月, 2019 // Host by 天池 // 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。
August, 13 - Oct 31, 2019 // Host by NeurIPS 2019 // Prize: $1,000USD
Note: Build the best AI bot to play reconnaissance blind chess, a challenge for making optimal decisions in the face of uncertainty. Reconnaissance blind chess is like chess except a player does not know where her opponent's pieces are a priori. Rather, she can covertly sense a chosen 3x3 square of the board each turn and also learn partial information from captures.
7月16 - 9月16, 2019 // Host by 天池 // 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。
ARIEL Data Challenge Series 2019
~ 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.
The VoxCeleb Speaker Recognition Challenge
July 15, 2019 - Sep. 14, 2019 // Host by CodaLab // Prize: NaN
Note: The goal of this challenge is to probe how well current methods can recognize speakers from speech obtained 'in the wild'. The challenge will consists of the following two tasks:
Audio only speaker verification - Fixed training data: This task requires that participants train only on the VoxCeleb2 dev dataset for which we have already released speaker verification labels. The dev dataset contains 1,092,009 utterances from 5,994 speakers.
Audio only speaker verification - Open training data: For the open training condition, participants can use the VoxCeleb datasets and any other data (including that which is not publicly released) except the challenge's test data
2nd 3D Face Alignment in the Wild Challenge - Dense Reconstruction from Video
July 4 - Aug 15 2019 // Host by CodaLab // 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
Dunhuang Image Restoration Challenge@ICCV2019 workshop on e-Heritage
Jul 25 - Aug 16, 2019 // Host by EvalAI & ICCV 2019 // Prize: NaN
Note: In 1970s, the Dunhuang Academy is established to systematically preserve the heritage. From the study, half of them suffer from corrosion and aging. Because the paintings are created by different artists from 10 centuries, it is non-trivial for manual restoration. And therefore, we release the first Dunhuang Challenge with 600 paintings, which enables an open and public attention in the research community on data driven e-heritage restoration.
This year, the academy is proposing to collaborate with Microsoft Research and other researchers over the world, aiming to solve the automatic restoration of the wall painting using computer vision and machine learning technology.
7月24日 - 9月21, 2019 // Host by 天池 // Prize: ￥300000
Note: 本次大赛要求参赛者根据智联招聘抽样的经过脱敏的求职者标签数据、职位信息、及部分求职者行为信息、用人单位反馈信息，训练排序模型，对求职者的职位候选集进行排序，尽可能使得双端都满意的职位（求职者满意以及用人单位满意）优先推荐。本次比赛里，假定对于曝光给求职者的职位候选集里，假如求职者感兴趣会产生浏览职位行为，浏览职位后，如果求职者满意会产生主动投递行为。用人单位收到求职者主动投递的简历后会给出是否满意的反馈信号。
Accurate Automated Spinal Curvature Estimation
Note: The goal of MICCAI 2019 Challenge on accurate automated spinal curvature estimation and error correction from x-ray images is to investigate (semi-)automatic spinal curvature estimation algorithms and provide a standard evaluation framework with a set of x-ray images.
AutoCV2: Image and video Classification
July 2 - Aug 20, 2019 // Host by AutoDL & NeurIPS 2019 // 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.
5月21日 - 10月14日, 2019 // Host by 讯飞开放平台 // 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。
QMUL Surveillance Face Recognition Challenge @ ICCV2019 workshop RLQ
27 June - 30 Aug, 2019 // Host by EvalAI & ICCV 2019 // 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.
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。
Game of Drones – Competition at NeurIPS 2019
July 1st – Dec. 8th, 2019 // Host by NeurIPS 2019 // Prize: ~12,000USD
Note: Game of Drones is a multi-drone racing tournament conducted in the high-fidelity simulation environment AirSim. Participants will have the choice of three tiers: Planning only, Perception only, or Full Autonomous Racing. The aim is to combine challenges from adversarial planning and real-time perception and to encourage fusing learning- and model-based approaches.
2019-07-17 至 2019-09-30 // Host by 之江实验室 // Prize: 大赛总奖金池超过260万元
Note: 随着新一轮世界科技革命和产业变革的孕育兴起，人工智能已经成为当前信息技术和未来科技高端发展的重要方向。为激发广大科研人员人工智能创业者参与人工智能前沿理论和算法研究的热情，之江实验室举办2019之江杯全球人工智能大赛，以“以赛引才、以赛促研、以赛兴业”为基本思路，聚焦人工智能“基础研究”+“产融结合”，促进我国人工智能发展走在世界前列引领科技发展潮流。
视频描述生成: 本赛题为视频描述（Video Caption）,视频描述的输入是一段视频，输出是描述视频主要故事的一段文本。
行人多目标跟踪: 主要任务是给定一个图像序列，找到图像序列中运动的物体，对目标进行定位，并将不同帧中的同一行人一一对应，记录其ID，然后给出不同物体的运动轨迹。
零样本目标检测: 零样本目标检测（zero-shot object detection）竞赛的任务是在已知类别上训练目标检测模型，但要求模型能够用于检测测试图片中未知类别的对象。
电商评论观点挖掘: 本次品牌评论观点挖掘的任务是在商品评论中抽取商品属性特征和消费者观点，并确认其情感极性和属性种类。
Challenge on Deep Learning based Loop Filter for Video Coding
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.
CoNLL 2019 Shard Task on Cross-Framework Meaning Representation Parsing
March 6 - November 3, 2019 // Host by CodaLab // 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.
Automatic Structure Segmentation for Radiotherapy Planning Challenge 2019
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.
Exoplanet imaging data challenge
May 16th - Sep 16th, 2019 // Host by CodaLab // 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).
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。
Peking University International Competition on Ocular Disease Intelligent Recognition (ODIR-2019)
May 18 - Sep 25, 2019 // Host by Grand Challenges & 北京大学 // 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。
NeurIPS 2019 : MineRL Competition
Note: The main task of the competition is solving the ObtainDiamond environment. In this environment, the agent begins in a random starting location without any items, and is tasked with obtaining a diamond. This task can only be accomplished by navigating the complex item hierarchy of Minecraft.
Digestive-System Pathological Detection and Segmentation Challenge 2019
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.
The 2nd China (Hengqin) International University Quantitative Finance Competition
2019-04-19 至 2020-03-21 // Host by 珠海市横琴新区金融服务中心 // 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.
Visual Domain Adaptation Challenge (VisDA-2019)
April 9 - Sept. 27, 2019 // Host by CodaLab & ICCV 2019 // 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
7月1日-10月23日, 2019 // Host by DC 竞赛 // 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。
7月1日-10月23日, 2019 // Host by DC 竞赛 // 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。
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).
Note: Fashion IQ is a new dataset we contribute to the research community to facilitate research on natural language based interactive image retrieval
MicroNet Challenge @NeurIPS 2019
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: 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.
2月28-12月28 2019 // Host by 杭州市人民政府 // Prize: TBA
Note:
中国移动专题赛: 赛题一：ETC便民服务群体挖掘; 赛题二：企业人才结构变化预测;
行业算法赛: 赛题一：楼盘精准推荐模型; 赛题二：社区独居老人识别与居民用能数据分析; 赛题三：移动办事服务的用户行为预测;
Multi-domain Task-Completion Dialog Challenge [DSTC 8]
June 17 - Oct 6, 2019 // Host by CodaLab & 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 methods for building a conversation model that generates appropriate domain-specific user responses to an incomplete dialog history.
Now - October 14, 2019 // Host by Kaggle // Prize: $15,000
Note: Opening the door to a thousand years of Japanese culture
NeurIPS 2019: Learn to Move - Walk Around
June 6 ~ October 27, 2019 // Host by crowdAI & NeurIPS 2019 // Prize: NVIDIA GPU + Travel grant
Note: You are provided with a human musculoskeletal model and a physics-based simulation environment, OpenSim.
There will be three tracks: 1) Best performance, 2) Novel ML solution, and 3) Novel biomechanical solution, where all the winners of each track will be awarded.
Graph Golf: The Order/degree Problem Competition
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.
Traffic4cast -- Traffic Map Movie Forecasting
May 1 - Dec 1, 2019 // Host by NeurIPS 2019 // Prize: ~17,000USD + 2 resea. fellowships up to 12 months + compl. registrations
Note: Predict high resolution traffic flow volume, heading, and speed on a whole city map looking 15 minutes into the future! Kicking off a series of annual competitions, this year's data is based on 100 billion probe points from 3 cities mapped in 5 minute intervals, showing trends across weekdays and seasonal effects. Improved traffic predictions are of great social, environmental, and economic value, while also advancing our general ability to capture the simple implicit rules underlying a complex system and model its future states.
Jul 31 - Oct 31, 2019 // Host by NeurIPS 2019 // Prize: $10,000USD
Note: A causal understanding of climatic interactions is of high societal relevance from identifying causes of extreme events to process understanding and weather forecasting. This competition comprises a number of multivariate time series datasets featuring major challenges of climate data from time delays and nonlinearity to nonstationarity and selection bias. The competition aims to open up new interdisciplinary research pathways by improving our scientific understanding of Earth’s climate, while also driving method development and benchmarking in the computer science community.
Automated Deep Learning (AutoDL)
Apr 29 - Oct 31, 2019 // Host by NeurIPS 2019 // Prize: ~$10,000USD
Note: The AutoDL challenge aims taking the automate the design of deep learning (DL) methods to solve generic tasks. This is a challenge with “code submission”: machine learning algorithms are trained and tested on a challenge platform on data invisible to the participants. We target applications such as speech, image, video, and text, for which DL methods have had great success recently, to drive the community to work on automating the design of DL models. Raw data will be provided, formatted in a uniform tensor manner, to encourage participants to submit generic algorithms. We will impose restrictions on training time and resources to push the state-of-the-art further. We will provide a large number of pre-formatted public datasets and set up a repository of data exchange to enable meta-learning.
Animal-AI Olympics Competition
January - December, 2019 // Host by EvalAI // Prize: NaN
Note: The Animal-AI Olympics is an AI competition with tests inspired by animal cognition. Participants are given a small environment with just seven different classes of objects that can be placed inside. In each test, the agent needs to retrieve the food in the environment, but to do so there are obstacles to overcome, ramps to climb, boxes to push, and areas that must be avoided. The real challenge is that we don't provide the tests in advance. It's up to you to play with the environment and build interesting setups that can help create an agent that understands how the environment's physics work and the affordances that it has. The final submission should be an agent capable of robust food retrieval behaviour similar to that of many kinds of animals. We know the animals can pass these tests, it's time to see if AI can too. The Animal-AI Olympics is an AI competition with tests inspired by animal cognition. Participants are given a small environment with just seven different classes of objects that can be placed inside. In each test, the agent needs to retrieve the food in the environment, but to do so there are obstacles to overcome, ramps to climb, boxes to push, and areas that must be avoided. The real challenge is that we don't provide the tests in advance. It's up to you to play with the environment and build interesting setups that can help create an agent that understands how the environment's physics work and the affordances that it has. The final submission should be an agent capable of robust food retrieval behaviour similar to that of many kinds of animals. We know the animals can pass these tests, it's time to see if AI can too.
3D Object Detection over HD Maps for Autonomous Cars
Nov 1 - Nov 7, 2019 // Host by NeurIPS 2019 // Prize: ~17,500USD
Note: Autonomous cars are expected to dramatically redefine the future of transportation. The 3D Perception system of the autonomous car is a critical keystone upon which high level autonomy functions depend. This competition is designed to help advance the state of the art in 3D object detection by focusing research on this topic in the context of autonomous cars, specifically by sharing the full modality of sensor data available to typical autonomous cars, and by providing access to a high fidelity HD map.
TBA – Nov 8, 2019 // Host by NeurIPS 2019 // Prize: ~15,000USD in Google Cloud Credits
Note: Pommerman: Train a team of communicative agents to play Bomberman in a partially observed setting. Compete against other teams.
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.
Geopolitical Forecasting [GF] Challenge 2
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.
ModaNet Fashion Understanding Challenge
Oct 1, 2018 - Dec 11, 2019 // Host by EvalAI // 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.
TBA – Dec 11, 2019 // Host by NeurIPS 2019 // Prize: 3 mo. Internship @IBM res. Africa
Note: In the NeurIPS Live Malaria Challenge we are looking for participants to apply machine learning tools to determine novel solutions which could impact malaria policy in Sub Saharan Africa. Specifically, how should combinations of interventions be deployed under budget constraints to impact lives saved and the prevalence of the malaria parasite in a simulated environment.
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.
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.
Crack the neural code of the brain
Note: The challenge goal is to classify the brain activity state of an animal based on spiking activity patterns of its individual neurons.
Spatiotemporal PM10 concentration prediction
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.
Note: The goal is to design an automatic Rubik's analyzer that estimates the current length of the shortest path to the solution.
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.
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).
March 15 - August 2, 2019 // Host by Grand Challenges // Prize: NaN
Note: The goal of this challenge is to accelerate the development of reliable kidney and kidney tumor semantic segmentation methodologies.
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
Feb 8, 2019 - Apr 26, 2099 // Host by EvalAI // 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.
No deadline // Host by Kaggle // Prize: NaN
Note: Final project for "How to win a data science competition" Coursera course
July 20, 2019 - Never // Host by CodaLab // Prize: NaN
Note: Unlike other QA datasets like SQuAD in which the answers are extractive, we allow the answers to be abstractive. The task requires model to read a short tweet and a question and outputs a text phrase (does not need to be in the tweet) as the answer.
Lexical Semantic Change Detection in German
July 1 - Never // Host by CodaLab // 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.)
YouCook2-BoundingBoxes Video Object Grounding Task
June 24, 2019 - Never // Host by CodaLab & Github // 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).
Oil Radish Semantic Segmentation and Yield Estimation Challenges
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.
Mobile age group classification
May. 17, 2019 - Never // Host by CodaLab // 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.
ActivityNet-Entities Object Localization Task
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, code repo, and CVPR 2019 oral paper.
YouCook2 Dense Video Captioning
May 6, 2019 - Never // Host by CodaLab // 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.
TVQA Test Public Evaluation (w/timestamp) Beta
Nov. 16, 2018 - Never // Host by CodaLab & 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).
DRIVE: Digital Retinal Images for Vessel Extraction
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.
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.
The Large Scale Vertebrae Segmentation Challenge (VerSe2019)
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
Vision and Language Navigation
Mar 13, 2018 - Dec 31, 2099 // Host by EvalAI // 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.
Jun 20, 2018 - Jun 22, 2100 // Host by EvalAI // 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.
SQuAD2.0: The Stanford Question Answering Dataset
Note: Stanford Question Answering Dataset (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.
CoQA: A Conversational Question Answering Challenge
Note: CoQA is a large-scale dataset for building Conversational Question Answering 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.
The Natural Language Decathlon: A Multitask Challenge for NLP
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).