2020 NeurIPS competition - 3D+Texture garment reconstruction
05/11/2020 - 10/03/2020 // Host by Codalab // Prize: $1,500 + NVIDIA GPU
Note: The dataset is an extension of CLOTH3D dataset including 3D garments, texture data and RGB rendering. The dataset contains more than 2M frames (8K+ sequences) of simulated and rendered garments in 7 categories:Tshirt, shirt, top, trousers, skirt, jumpsuit and dress. Garments are simulated on top of SMPL model using MoCap data processed into SMPL params. Garments may have between 3.7K-17.2K vertices with variable fabric, shape, tightness and topology. Therefore, each garment can show a different dynamic on top of a fixed pose sequence. Renderings are performed with Cycles engine (Blender) with multiple light sources to simulate indoor/outdoor perceptions, and different texture patterns or plain color. Finally, for extra realism and unbiasedness, human skin, hair and eyes are also provided with random tones.
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2020 DIGIX全球校园AI算法精英大赛
2020/07/08 - 2020/10/31 // Host by DC竞赛 & HuaWei // Prize: 158,000 RMB
Note: 赛道A:机器学习赛道 1)赛题一:广告CTR预测 2)赛题二:搜索相关性预测; 赛道B:计算机视觉赛道 赛题:数码设备图像检索
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Google Landmark Recognition 2020
Now - October 6, 2020 // Host by Kaggle // Prize: 25,000 USD
Note: In this competition, you are asked to take test images and recognize which landmarks (if any) are depicted in them. The training set is available in the train/ folder, with corresponding landmark labels in train.csv. The test set images are listed in the test/ folder. Each image has a unique id. Since there are a large number of images, each image is placed within three subfolders according to the first three characters of the image id (i.e. image abcdef.jpg is placed in a/b/c/abcdef.jpg).
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中文医学文本命名实体识别
2020-07-10 - 2020-09-29 // Host by Biendta
Note: 本评测任务为面向中文医学文本的命名实体识别,即给定schema及句子sentence,对于给定的一组纯医学文本文档,任务的目标是识别并抽取出与医学临床相关的实体,并将他们归类到预先定义好的类别。将医学文本命名实体划分为九大类,包括:疾病,临床表现,药物,医疗设备,医疗程序,身体,医学检验项目,微生物类,科室。标注之前对文章进行自动分词处理,所有的医学实体均已正确切分。
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中文医学文本实体关系抽取
2020-07-10 - 2020-09-29 // Host by Biendta
Note: 实体和关系抽取作为信息抽取的重要子任务,近些年众多学者利用多种技术在该领域开展深入研究。将这些技术应用于医学领域,抽取非结构化和半结构化的医学文本构建成医学知识图谱,可服务于下游子任务。非结构化的医学文本,如医学教材每一个自然段落,临床实践中每种疾病下的主题,电子病历数据中的主诉、现病史、鉴别诊断等,都是由中文自然语言句子或句子集合组成。实体关系抽取是从非结构化医学文本中找出医学实体,并确定实体对关系事实的过程。
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DSTC9 - The Ninth Dialog System Technology Challenge
June 4 - Oct 19, 2020 // Host by Codalab
Note: Starting as an initiative to provide a common testbed for the task of Dialog State Tracking, the first Dialog State Tracking Challenge (DSTC) was organized in 2013, followed by DSTC2_3 in 2014, DSTC4 in 2015, and DSTC5 in 2016. Given the remarkable success of the first five editions, and understanding both, the complexity of the dialog phenomenon and the interest of the research community in a wider variety of dialog related problems, the DSTC rebranded itself as "Dialog System Technology Challenges" for its sixth edition. Then, DSTC6, DSTC7, and DSTC8 have been completed in 2017, 2018, and 2019 respectively.
In this ninth challenge, the call for task proposals has resulted into the following four different tracks:(1) Beyond Domain APIs:Task-oriented Conversational Modeling with Unstructured Knowledge Access, (2) Multi-domain Task-oriented Dialog Challenge II, (3) Interactive Evaluation of Dialog, and (4) SIMMC:Situated Interactive Multi-Modal Conversational AI. The objective of these tracks is to invite interested organizations conduct dialog related challenges in specific areas of research and under the umbrella of the DSTC.
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OSIC Pulmonary Fibrosis Progression
Now - October 6, 2020 // Host by Kaggle // Prize: 55,000 USD
Note: The aim of this competition is to predict a patient’s severity of decline in lung function based on a CT scan of their lungs. Lung function is assessed based on output from a spirometer, which measures the forced vital capacity (FVC), i.e. the volume of air exhaled.
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MAFAT Radar Challenge
July 15th - November 1st, 2020 // Host by Codalab // Prize: 40,000 USD
Note: The competition’s objective is to explore automated, novel solutions that will enable the classification of humans and animals that are tracked by radar systems with a high degree of confidence and accuracy. The participants’ goal is to classify segments of radar tracks of humans or animals using the I/Q signal matrix as an input. The task at hand is a binary classification task; the tracked objects are either humans or animals.
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Predict Playing Time of J League Players
Now - Dec 31 2020 // Host by Nishika // Prize: 16万円
Note: The past few years of professional football players in the J1 League. From the data, you will build a machine learning model to predict the 2019 game time for each player.
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Efficient Open-Domain Question Answering 高效开放域问答(NeurIPS 2020)
July - October 14, 2020 // Host by NeurIPS 2020 & Google AI // Prize: Will be awarded...
Note: This competition will be evaluated using the open domain variant of the Natural Questions question answering task. The questions in Natural Questions are real Google search queries, and each is paired with up to five reference answers. The challenge is to build a question answering system that can generate a correct answer given just a question as input.
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Diagnostic Questions - NeurIPS 2020
July 15th - November 15 2020 // Host by Codalab & NeurIPS 2020
Note: In this competition, participants will focus on the students' answer records to these multiple-choice diagnostic questions, with the aim of 1) accurately predicting which answers the students provide; 2) accurately predicting which questions have high quality; and 3) determining a personalized sequence of questions for each student that best predicts the student's answers. These tasks closely mimic the goals of a real-world educational platform and are highly representative of the educational challenges faced today. We provide over 20 million examples of students' answers to mathematics questions from Eedi, a leading educational platform which thousands of students interact with daily around the globe. Participants to this competition have a chance to make a lasting, real-world impact on the quality of personalized education for millions of students across the world.
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Predicting Generalization in Deep Learning - NeurIPS 2020
July 15th - October 31th, 2020 // Host by Codalab & NeurIPS 2020
Note: The participants of this competition are required to submit a single python script that contains a function named complexity. The function should take in a Keras model and a keras dataset object which contains the training data of the particular model, and output a single real-valued scalar. This function will be run on all the models in the private dataset in a Docker container on our server.
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AnDi - The anomalous diffusion challenge
March 1, 2020 - Nov. 1, 2020 // Host by CodaLab
Note: The AnDi challenge aims at bringing together a vibrating and multidisciplinary community of scientists working on this problem. The use of the same reference datasets will allow an unbiased assessment of the performance of published and unpublished methods for characterizing anomalous diffusion from single trajectories.
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L2RPN 2020 - Learning To Run a Power Network Challenge
Jul 8 - Oct 30, 2020 // Host by Codalab // Prize: 15,000 USD
Note: Power networks transport electricity across states, countries and even continents. They are the backbone of power distribution, playing a central economical and societal role by supplying reliable power to industry, services, and consumers. Their importance appears even more critical today as we transition towards a more sustainable world within a carbon-free economy, and concentrate energy distribution in the form of electricity. Problems that arise within the power grid range from transient brownouts to complete electrical blackouts which can create significant economic and social perturbations, i.e.de facto freezing society. Grid operators are still responsible for ensuring that a reliable supply of electricity is provided everywhere, at all times. With the advent of renewable energy, electric mobility, and limitations placed on engaging in new grid infrastructure projects, the task of controlling existing grids is becoming increasingly difficult, forcing grid operators to do “more with less”. This challenge aims at testing the potential of AI to address this important real-world problem for our future.
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Hateful Memes
05/11/2020 - 10/31/2020 // Host by DrivenData & FACEBOOK // Prize: $100,000
Note: Your goal is to predict whether a meme is hateful or non-hateful. This is a binary classification problem with multimodal input data consisting of the the meme image itself (the image mode) and a string representing the text in the meme image (the text mode).
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2020链想家计算科技大赛 - 人工智能赛组
2020-05-20 - 2020-11-15 // Host by Biendta // Prize: 15 万 + 10 万 RMB
Note:
MoocCube学生行为分析挑战赛:参赛选手需要根据学生的课堂表现,观看视频,参与答题情况,预测学堂在线的某一名学生是否中途退课。
同名消岐挑战赛:参赛选手需要在给定的拥有同名作者的论文中,识别出哪些同名作者的论文属于同一个人。
COVID-19 新冠知识图谱构建挑战赛:参赛选手需要根据提供的数据集构建 COVID-19 相关知识图谱。
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Deep Learning for Geometric Computing - ABC Geometry Reconstruction Challenge
June 1 - Dec. 31, 2020 // Host by Codalab & CVPR2020
Note: The ABC Challenge serves as a testbed for common shape analysis and geometry processing tasks. We supplement the challenge with additional software libraries, sets of large-scale standardized benchmarks (data splits, resolutions, and targets), and implementations of evaluation metrics.
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The NLC2CMD Challenge (自动写脚本比赛) - NeurIPS 2020
July - Dec 11, 2020 // Host by NeurIPS 2020 // Prize: 2,500+ USD
Note: The NLC2CMD Competition brings the power of natural language processing to the command line. You are tasked with building models that can transform descriptions of command line tasks in English to their Bash syntax.
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Smart meter is coming
From Jan. 6, 2020 to Dec. 11, 2020 // Host by Challenge data
Note: The goal is to train an algorithm to replace many monitoring systems which are too intrusive and too expensive. This challenge is known as NILM (Nonintrusive load monitoring) or NIALM (Nonintrusive appliance load monitoring). The aim of the challenge is to find the part of electric consumption in one household dedicated to 4 appliances (washing machine, fridge_freezer, TV, kettle). There are no time constraints. The past and the future are known.
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Defect Prediction on production lines
From Jan. 6, 2020 to Dec. 18, 2020 // Host by Challenge data
Note: The goal of the challenge is to predict defect on starter motor production lines. During production samples assembly, different values (torques, angles ...) are measured on different mounting stations. At the end of the line, additional measures are performed on two test benches in order to isolate defects. As a result, samples are tagged ‘OK’, ‘KO’. We would like to design a model that could identify such defects before the test bench step.
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Rakuten France Multimodal Product Data Classification
From Jan. 6, 2020 to Dec. 18, 2020 // Host by Challenge data
Note: The goal of this data challenge is large-scale multimodal (text and image) product data classification into product type codes.
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AI for Meter Reading
From Jan. 6, 2020 to Dec. 18, 2020 // Host by Challenge data
Note: The goal of this challenge is to design an algorithm reading the consumption index from a valid picture of a meter.
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NLP applied to judicial decisions parsing
From Jan. 6, 2020 to Dec. 18, 2020 // Host by Challenge data
Note: At Predilex, we have “jurisprudence” data as text files and we want to build an algorithm to automate the extraction of the relevant information. In this challenge, we want to extract from "jurisprudence" the sex of the victim, the date of the accident and the date of the consolidation of the injuries.
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Metamodels to improve energy consumptions and comfort control in big buildings
From Jan. 6, 2020 to Dec. 18, 2020 // Host by Challenge data
Note: This challenge aims at introducing a new statistical model to predict and analyze energy consumptions and temperatures in a big building using observations stored in the Oze-Energies database. Physics-based approaches to build an energy/temperature simulation tool in order to model complex building behaviors are widespread in the most complex situations.
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Predicting lung cancer survival time
From Jan. 6, 2020 to Dec. 18, 2020 // Host by Challenge data
Note: The challenge proposed by Owkin is a supervised survival prediction problem:predict the survival time of a patient (remaining days to live) from one three-dimensional CT scan (grayscale image) and a set of pre-extracted quantitative imaging features, as well as clinical data.
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Where will the next trade take place?
From Jan. 6, 2020 to Dec. 18, 2020 // Host by Challenge data
Note: Given recent trades and order books from a set of trading venues, predict on which trading venue the next trade will be executed.
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Prediction of direction of Bitcoin based on sentiment data
From Jan. 6, 2020 to Dec. 18, 2020 // Host by Challenge data
Note: The problem is a classification challenge that aims at building investment strategies on cryptocurrencies based on sentiment extracted from news and social networks.
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Predict sex from brain rythms
From Jan. 6, 2020 to Dec. 18, 2020 // Host by Challenge data
Note: In this dataset, we try to predict the gender of someone based on 40 windows of 2 seconds taken during sleep.
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Stock Return Prediction
From Jan. 6, 2020 to Dec. 18, 2020 // Host by Challenge data
Note: The proposed challenge aims at predicting the return of a stock in the US market using historical data over a recent period of 20 days.
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PhotoRoom Object Segmentation from Synthetic Images
From Jan. 6, 2020 to Dec. 18, 2020 // Host by Challenge data
Note: Provided with an image of an object, the goal is to segment the salient (main) object in the scene. For each pixel of the input image, the model must categorize it as foreground or background.
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Dyni Odontocete Click Classification, 10 species [ DOCC10 ]
From Jan. 6, 2020 to Dec. 18, 2020 // Host by Challenge data
Note: The goal is to classify each click according to the corresponding emitting species.
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Asset production estimation
From Jan. 6, 2020 to Dec. 18, 2020 // Host by Challenge data
Note: The goal of this problem is to estimate the production of a group of industrial assets, based on daily measurements and capacity constraints.
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Deep Hedging for an Equinoxe
From Jan. 6, 2020 to Dec. 18, 2020 // Host by Challenge data
Note: The goal is to determine 3 strategies associated with 3 different limit L1:We remind you that L1 is limit of the total sensitvity of the portfolio made of the Equinox, the vega hedge instruments (vanilla option) and the delta hedge (strait position in the underlying) with respect to the underlying.
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Wind Power forecasting for the day-ahead energy market
From Jan. 1, 2020 to Dec. 31, 2020 // Host by Challenge data
Note: The goal of this challenge is to predict the energy production of six WF owned by CNR. Each WF production will be individually predicted, using meteorological forecasts as input. Predictions will focus on the day-ahead energy production (hourly production forecasts from day D+1 00h to day D+2 00h).
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Predicting response times of the Paris Fire Brigade vehicles
From June 10, 2019 to Dec. 31, 2020 // Host by Challenge data
Note: Your task will be to predict the delay between the selection of a rescue vehicle (the time when a rescue team is warned) and the time when it arrives at the scene of the rescue request (manual reporting via portable radio).
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COVID-19チャレンジ(フェーズ1)
2020/3/19 - COVID-19の日本国内収束(新規罹患者が0) // Host by Signate // Prize: 社会貢献・名誉・著作クレジット
Note: まず、フェーズ1として、日本国内のCOVID-19罹患者数と患者間の関係データに関する、マシンリーダブルかつデータ分析可能な最大規模のデータセットの構築を目指します。続けてフェーズ2では、そのデータセットを用い、様々な統計学的手法によるデータ分析を実施し、感染実態に迫るインサイト抽出を目指します。
Published by: DataSciCamp
Entry Deadline:

COVID-19チャレンジ(フェーズ2)
2020/3/19 - COVID-19の日本国内収束(新規罹患者が0) // Host by Signate // Prize: 社会貢献・名誉・著作クレジット
Note: そこで、Phase2では、Phase1で得られたデータをもとに、社会距離戦略の意思決定に資する(少なくとも議論の題材になる)データ分析を目指します。データ分析の過程における、不足データや分析困難なフィールドについては、Phase1にフィードバックし、データ収集や構造化を進めていきます。ただし、報道ベースのデータ収集では情報精度に限界がありますので、確実に実施可能(新規データ追加に関しては収集可能であることが確認されているもの)なもののみフィードバック願います。
Published by: DataSciCamp
Entry Deadline:

COVID-19チャレンジ(フェーズ3)
2020/5/11 - COVID-19の日本国内収束(新規罹患者が0) // Host by Signate // Prize: 社会貢献・名誉・著作クレジット
Note: そこで、Phase3におけるチャレンジでは、有用な罹患者数推移予測手法の検討、および予測に有効な特徴の探索を目指します。予測に有用な特徴が必ずしも因果性がある(つまり、説明能力の高い因子、あるいは対策することで罹患者数を減らすことができる因子)わけではありませんが、何かの手がかりになればと考えています。
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Entry Deadline:

The 4th AI Edge Contest (Implementation Contest 2)
Now - Dec 31 2020 // Host by Signate // Prize: TBA
Note: As a general rule, in accordance with Article 4, Paragraph 1 of the terms of participation, diclosing any contents such as insights and deliverables transmitted through the information or data provided by our company in relation to this competition is not permitted, however, only after the completion of this competition and for non-commercial purposes, it will be possible to disclose the contents within the score of the table below.
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VizWiz-Captions Challenge 2020
Jan 22, 2020 - Jun 22, 2100 // Host by EvalAI
Note: Observing that people who are blind have relied on (human-based) image captioning services to learn about images they take for nearly a decade, we introduce the first image captioning dataset challenge to represent this real use case. This dataset challenge, which we call VizWiz-Captions, consists of over 39,000 images originating from people who are blind that are each paired with 5 captions. Our proposed challenge addresses the task of predicting a suitable caption given an image.
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VizWiz-Visual Question Answering Challenge 2020
Jan 22, 2020 - Jun 22, 2100 // Host by EvalAI
Note: A natural application of computer vision is to assist blind people, whether that may be to overcome their daily visual challenges or break down their social accessibility barriers. VizWiz-VQA is proposed to empower a blind person to directly request in a natural manner what (s)he would like to know about the surrounding physical world.
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decaNLP
Now - Never // Host by Salesforce
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). Each task is cast as question answering, which makes it possible to use our new Multitask Question Answering Network (MQAN). This model jointly learns all tasks in decaNLP without any task-specific modules or parameters in the multitask setting. For a more thorough introduction to decaNLP and the tasks, see the main website, our blog post, or the paper.
Published by: DataSciCamp
No Deadline.

Multi-FC
Aug. 29, 2019 - Never // Host by CodaLab
Note: The MultiFC is the largest publicly available dataset of naturally occurring factual claims for the purpose of automatic claim verification. It is collected from 26 English fact-checking websites, paired with textual sources and rich metadata, and labeled for veracity by human expert journalists. In the figure below you can see one example of a claim instance. Entities are obtained via entity linking. Article and outlink texts, evidence search snippets and pages are not shown.
Published by: DataSciCamp
No Deadline.

MinneApple - A Benchmark Dataset for Apple Detection and Segmentation
2019-11-01 ~ Never // Host by CodaLab
Note: To guarantee a fair comparison of your approach with others we have setup codalab competitions for fruit detection, fruit segmentation and fruit counting.
Published by: DataSciCamp
No Deadline.

TVR Dataset
Jan 20, 2020 - Never // Host by CodaLab
Note: TV show Retrieval is a new multimodal retrieval task, in which a short video moment has to be localized from a large video (with subtitle) corpus, given a natural language query. Its associated TVR dataset is a large-scale, high-quality dataset consisting of 108,965 queries on 21,793 videos from 6 TV shows of diverse genres, where each query is associated with a tight temporal alignment.
Published by: DataSciCamp
No Deadline.

TVQA DATASET
2019-04 - Never // Host by CodaLab
Note: 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). It consists of 152.5K QA pairs from 21.8K video clips, spanning over 460 hours of video. The questions are designed to be compositional, requiring systems to jointly localize relevant moments within a clip, comprehend subtitles-based dialogue, and recognize relevant visual concepts.
Published by: DataSciCamp
No Deadline.

WiC-TSV (Target Sense Verification For Words In Context) Challenge
March 8 - May 2, 2020 // Host by CodaLab
Note: Formally, WiC is framed as a binary classification task. Each instance in WiC-TSV consists of a target word w with a corresponding target sense s represented by either its definition (subtask 1) or its hypernym/s (subtask 2), and a context c containing the target word w. The task aims to determine whether the meaning of the word w used in the context c matches the target sense s.
Published by: DataSciCamp
No Deadline.

Twitter RecSys Challenge 2020 (Twitter 参与率预测+内容推荐)
Now - Never // Host by Twitter // Prize: $35,000
Note: On the platform, users post and engage with (in the form of Likes, Replies, Retweets and Retweets with comments) content known as “Tweets”. This challenge aims to evaluate novel algorithms for predicting different engagement rates at a large scale, and push the state-of-the-art in recommender systems. Following the success and advancements in the domain of top-K recommendations, we aim to encourage the development of new approaches by releasing the largest real-world dataset to predict user engagements. The dataset comprises of roughly 200 million public engagements, along with user and engagement features, that span a period of 2 weeks and contain public interactions (Like, Reply, Retweet and Retweet with comment), as well as 100 million pseudo negatives which are randomly sampled from the public follow graph. While sampling the latter pool of Tweets, we take special care about preserving user privacy.
Published by: DataSciCamp
No Deadline.

SemanticKITTI - A Dataset for Semantic Scene Understanding using LiDAR Sequences
2020-04-01 - Never // Host by Codalab
Note: ​We furthermore provide with the data also a benchmark suite covering different aspects of semantic scene understanding at different levels of granularity. To ensure unbiased evaluation of these tasks, we follow the common best practice to use a server-side evaluation of the test set results, which enables us to keep the test set labels private.
Published by: DataSciCamp
No Deadline.

HybridQA Competition
April 16, 2020 - Never // Host by Codalab
Note: A large-scale multi-hop question answering dataset over heterogenesous information of both structured tabular and unstructured textual forms.
Published by: DataSciCamp
No Deadline.

3D Poses in the Wild (3DPW) Challenge
May 01 - July 23, 2020 // Host by Codalab
Note: The "3D Poses in the Wild dataset" is the first dataset in the wild with accurate 3D poses for evaluation. While other datasets outdoors exist, they are all restricted to a small recording volume. 3DPW is the first one that includes video footage taken from a moving phone camera.
Published by: DataSciCamp
No Deadline.

Pathology Visual Question Answering (PathVQA)
To be announced. // Host by grand-challenge
Note: Is it possible to develop an "AI Pathologist" to pass the board-certified examination of the American Board of Pathology? To achieve this goal, we launch this medical visual question answering (VQA) challenge where deep learning models are to be developed for interpreting pathology images and answering questions about the image contents. The task is VQA on pathology images:given a pathology image and a question, the model needs to give the correct answer. We have collected a dataset containing 4,998 images and 32,799 question-answer pairs. Half of these questions are open-ended (why, what, how, where, etc.) and the other half are “yes/no” questions.
Published by: DataSciCamp
No Deadline.

LoDoPaB-CT
To be announced. // Host by grand-challenge & Code Sprint 2020
Note: The task of this challenge is to reconstruct CT images of the human lung from (simulated) low photon count measurements. For evaluation, the PSNR and SSIM values are computed w.r.t. the images that were used as ground truth.
Published by: DataSciCamp
No Deadline.

Apples-CT
To be announced. // Host by grand-challenge & Code Sprint 2020
Note: The task of this challenge is to reconstruct CT images of apples with internal defects using as few as possible (simulated) measurements, followed by performing a semantic segmentation of four types of internal defects.
Published by: DataSciCamp
No Deadline.