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‎.vscode/tags

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‎_data/_effective_EvalAI.yaml

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pubtime: '2019-07-06'
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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.'
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prize: NaN
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- id: AnimalAIOlympics
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type1:
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- PF
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- AC
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type2:
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- RL
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title: 'Animal-AI Olympics Competition'
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url: http://animalaiolympics.com
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hostby:
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- EvalAI: https://evalai.cloudcv.org/web/challenges/challenge-page/396/overview
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range: January - December, 2019
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deadtime: "2019-11-01 23:59:59"
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timezone: UTC
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pubtime: '2019-07-23'
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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. "
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prize: NaN
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- id: Dunhuangimagerestoration
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type1:
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- PF
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- AC
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type2:
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- CV
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title: 'Dunhuang Image Restoration Challenge@ICCV2019 workshop on e-Heritage'
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url: http://www.eheritage-ws.org
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hostby:
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- EvalAI: https://evalai.cloudcv.org/web/challenges/challenge-page/402/
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- ICCV 2019: http://iccv2019.thecvf.com
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range: Jul 25 - Aug 16, 2019
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deadtime: "2019-08-16 23:59:59"
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timezone: UTC
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pubtime: '2019-07-23'
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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.
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<br>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.'
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prize: NaN

‎_data/_effective_codalab.yaml

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prize: NaN
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- id: TweetQA
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type1:
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- PF
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- AC
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type2:
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- NLP
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title: TweetQA Competition
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url: https://tweetqa.github.io
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hostby:
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- CodaLab: https://competitions.codalab.org/competitions/20307
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range: July 20, 2019 - Never
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deadtime: 'No deadline'
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timezone: UTC
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pubtime: '2019-07-23'
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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.'
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prize: NaN
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- id: AIM2019
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type1:
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- PF
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- AC
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type2:
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- CV
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title: 'AIM 2019 image manipulation challenges'
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url: http://www.vision.ee.ethz.ch/aim19/
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hostby:
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- CodaLab: https://competitions.codalab.org/
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- ICCV 2019: http://iccv2019.thecvf.com
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range: July 17, 2019 - Aug. 30, 2019
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deadtime: "2019-08-23 23:59:59"
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timezone: UTC
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pubtime: '2019-07-23'
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note: 'Advances in Image Manipulation workshop and challenges on image and video manipulation in conjunction with ICCV 2019.
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<br>AIM 2019 image manipulation challenges:
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<br><a href="https://competitions.codalab.org/competitions/20156">Bokeh Effect Challenge: Track 1 Fidelity</a>;
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<br><a href="https://competitions.codalab.org/competitions/20157">Bokeh Effect Challenge: Track 2 Perceptual</a>;
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<br><a href="https://competitions.codalab.org/competitions/20158">RAW-to-RGB Mapping Challenge: Track 1 Fidelity</a>;
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<br><a href="https://competitions.codalab.org/competitions/20159">RAW-to-RGB Mapping Challenge: Track 2 Perceptual</a>;
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<br><a href="https://competitions.codalab.org/competitions/20163">Real World Super-Resolution Challenge: Track 1 Same Domain</a>;
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<br><a href="https://competitions.codalab.org/competitions/20164">Real World Super-Resolution Challenge: Track 2 Target Domain</a>;
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<br><a href="https://competitions.codalab.org/competitions/20165">Demoireing Challenge: Track 1 Fidelity</a>;
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<br><a href="https://competitions.codalab.org/competitions/20166">Demoireing Challenge: Track 2 Perceptual</a>;
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<br><a href="https://competitions.codalab.org/competitions/20167">Constrained Super-Resolution Challenge: Track 1 Parameters optimization</a>;
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<br><a href="https://competitions.codalab.org/competitions/20168">Constrained Super-Resolution Challenge: Track 2 Inference optimization</a>;
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<br><a href="https://competitions.codalab.org/competitions/20169">Constrained Super-Resolution Challenge: Track 3 Fidelity optimization</a>;
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<br><a href="https://competitions.codalab.org/competitions/20235">Extreme Super-Resolution Challenge: Track 1 Fidelity</a>;
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<br><a href="https://competitions.codalab.org/competitions/20236">Extreme Super-Resolution Challenge: Track 2 Perceptual</a>;
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<br>AIM 2019 video manipulation challenges:
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<br><a href="https://competitions.codalab.org/competitions/20246">Video Quality Mapping Challenge : Track 1 Supervised</a>;
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<br><a href="https://competitions.codalab.org/competitions/20247">Video Quality Mapping Challenge : Track 2 Unsupervised</a>;
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<br><a href="https://competitions.codalab.org/competitions/20248">Video Extreme Super-Resolution Challenge: Track 1 Fidelity</a>;
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<br><a href="https://competitions.codalab.org/competitions/20249">Video Extreme Super-Resolution Challenge: Track 2 Perceptual</a>;
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<br><a href="https://competitions.codalab.org/competitions/20244">Video Temporal Super-Resolution Challenge</a>;
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'
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prize: NaN
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- id: VoxCeleb
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type1:
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- PF
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- AC
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type2:
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- SP
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- CV
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title: The VoxCeleb Speaker Recognition Challenge
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url: http://www.robots.ox.ac.uk/~vgg/data/voxceleb/competition.html
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hostby:
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- CodaLab: https://competitions.codalab.org/competitions/20199
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range: July 15, 2019 - Sep. 14, 2019
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deadtime: "2019-08-15 23:59:59"
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timezone: UTC
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pubtime: '2019-07-23'
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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:
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<br>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.
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<br>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"
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prize: NaN
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- id: Reconstruction2D3D
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type1:

‎_data/_effective_dcjingsai.yaml

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# dcjingsai ##
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# http://www.dcjingsai.com/
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- id: IEEE_ISI_World_Cup_2019
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type1:
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- AC

‎_data/_effective_grand_challenge.yaml

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- id: AASCE2019
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type1:
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- PF
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- AC
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type2:
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- CV
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title: 'Accurate Automated Spinal Curvature Estimation'
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url: https://aasce19.grand-challenge.org
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hostby:
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- Grand Challenges: https://aasce19.grand-challenge.org
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- MICCAI 2019: https://www.miccai2019.org
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range: July 8 - Aug 20, 2019
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deadtime: '2019-08-20 23:59:59'
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timezone: UTC
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pubtime: '2019-07-23'
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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. '
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prize: NaN
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# TBA
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# - id: age_grand2019
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# type1:

‎_data/_effective_kaggle.yaml

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# https://www.kaggle.com/competitions
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- id: kuzushiji-recognition
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type1:
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- PF
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- AC
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type2:
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- NLP
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title: 'Kuzushiji Recognition'
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url: 'https://www.kaggle.com/c/kuzushiji-recognition'
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hostby:
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- Kaggle: https://www.kaggle.com/
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range: Now - October 14, 2019
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deadtime: "2019-10-07 23:59:59"
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timezone: UTC
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pubtime: '2019-07-23'
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note: 'Opening the door to a thousand years of Japanese culture'
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prize: $15,000
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- id: ieee-fraud-detection
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type1:
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- PF
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- AC
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type2:
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- DM
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title: 'IEEE-CIS Fraud Detection'
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url: 'https://www.kaggle.com/c/ieee-fraud-detection'
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hostby:
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- Kaggle: https://www.kaggle.com/
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- IEEE-CIS: https://cis.ieee.org
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range: Now - October 1, 2019
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deadtime: "2019-09-24 23:59:59"
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timezone: UTC
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pubtime: '2019-07-23'
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note: 'Can you detect fraud from customer transactions?'
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prize: $25,000
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- id: open-images-2019-instance-segmentation
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type1:
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- PF
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- AC
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type2:
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- CV
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title: 'Open Images 2019 - Instance Segmentation'
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url: 'https://www.kaggle.com/c/open-images-2019-instance-segmentation'
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hostby:
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- Kaggle: https://www.kaggle.com/
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- ICCV 2019: http://iccv2019.thecvf.com
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range: Now - October 27, 2019
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deadtime: "2019-09-24 23:59:59"
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timezone: UTC
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pubtime: '2019-07-23'
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note: 'Outline segmentation masks of objects in images'
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prize: $20,000
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- id: generative-dog-images
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type1:

‎_data/_effective_others.yaml

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prize: 40,000 元 x 2
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- id: zhijiangbei2019
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type1:
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- PF
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type2:
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- CV
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- DM
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- NLP
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title: '2019之江杯全球人工智能大赛'
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url: http://aicup2019.zhejianglab.com
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hostby:
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- 之江实验室: http://www.zhejianglab.com
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range: 2019-07-17 至 2019-09-30
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deadtime: "2019-08-31 23:59:59"
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timezone: "Asia/Shanghai"
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pubtime: '2019-07-23'
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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。
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<br><a href="https://zhejianglab.aliyun.com/entrance/231734/introduction?spm=5176.12281949.1003.1.2b58c341xkeLkZ">视频描述生成</a>: 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。
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<br><a href="https://zhejianglab.aliyun.com/entrance/231733/introduction?spm=5176.12281949.1003.2.2b58c341xkeLkZ">行人多目标跟踪</a>: 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。
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<br><a href="https://zhejianglab.aliyun.com/entrance/231732/introduction?spm=5176.12281949.1003.3.2b58c341xkeLkZ">零样本目标检测</a>: 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。
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<br><a href="https://zhejianglab.aliyun.com/entrance/231731/introduction?spm=5176.12281949.1003.4.2b58c341xkeLkZ">电商评论观点挖掘</a>: 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。
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'
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prize: 大赛总奖金池超过260万元
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- id: AIIA2019
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type1:
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- PF
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type2:
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- CV
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- DM
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title: '2019 AIIA杯人工智能巡回赛 中国移动“家·网”赛站'
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url: http://aiia.cmri.cn
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hostby:
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- 中国移动: http://open.home.10086.cn/
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range: 7月4-9月 2019
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deadtime: "2019-08-04 23:59:59"
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timezone: "Asia/Shanghai"
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pubtime: '2019-07-23'
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note: '结合中国移动在AI领域的研发布局，本次“家·网”赛站的主题是智慧家庭和智慧网络，希望借助AI技术构建数字家庭生态，打造动态高效的智能网络。
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<br><a href="http://open.home.10086.cn/hack/#/protal">智慧家庭赛题</a>
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<br><a href="http://aiia.cmri.cn/index/content_page">智慧网络赛题</a>: 任务一：网络流量预测; 任务二：无线侧故障根因分析;
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'
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prize: 240,000 元
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- id: quanqiushujuziyuankaifazhe
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type1:
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- PF
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type2:
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- DM
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title: '全球数据资源开发者大赛'
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url: https://wdd.datarda.com/index
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hostby:
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- 杭州市人民政府: http://www.hangzhou.gov.cn
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range: 2月28-12月28 2019
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deadtime: "2019-10-01 23:59:59"
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timezone: "Asia/Shanghai"
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pubtime: '2019-07-23'
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note: '
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<br>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;
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<br>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;
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'
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prize: TBA
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# http://www.prcv2019.com/竞赛通知.html
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# http://spiechallenges.cloudapp.net/competitions/

‎_data/_effective_天池.yaml

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# https://tianchi.aliyun.com/competition
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- id: alibabazhilianzhaopin
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type1:
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- PF
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type2:
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- DM
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title: '阿里巴巴大数据智能云上编程大赛 —— 智联招聘人岗智能匹配'
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url: https://tianchi.aliyun.com/competition/entrance/231728/introduction
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hostby:
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- 天池: 'https://tianchi.aliyun.com/'
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range: 7月24日 - 9月21, 2019
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deadtime: "2019-08-20 09:59:59"
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timezone: "Asia/Shanghai"
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pubtime: '2019-07-23'
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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。'
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prize: ￥300000
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- id: zhongjianjian
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‎_data/_hosts.yaml

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- id: crowdai
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type1:
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- PF
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title: crowdAI
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title: crowdAI (shutting down)
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url: https://www.crowdai.org
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logo: https://i.loli.net/2019/02/10/5c5fab0aa7cc0.png
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descri: "crowdAI is a platform for open data science challenges. crowdAI helps universities, government agencies, NGOs, or businesses to run and manage their data challenges. The crowdAI platform is a non-profit, open source infrastructure that can immediately reach thousands of data scientists around the world to work on interesting data problems."
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- id: aicrowd
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type1:
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- PF
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title: AIcrowd
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url: https://www.aicrowd.com
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logo: https://i.loli.net/2019/07/23/5d36619fc463991441.png
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descri: 'AIcrowd is a platform for streamlining your AI workflow - internally, or externally, by running AI, machine learning, and other data science challenges. AIcrowd helps organizations - whether businesses, universities, government agencies or NGOs - develop, manage, and promote their challenges. '
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- id: EvalAI
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descri: 'iChallenge is set up to share high-quality labeled and annotated imaging data of ophthalmology, to enhance communication between different researchers (not only computer scientists but also clinicians) and to promote development of automatic algorithms in <b>diagnosis and image segmentation</b>.
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<br>Contact: ai@baidu.com'
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- id: baiduaistudio
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type1:
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- PF
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title: 'Baidu大脑 | AI Studio'
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url: https://aistudio.baidu.com/aistudio/index
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logo: https://i.loli.net/2019/07/23/5d371b5fc124b88704.png
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descri: 'AI Studio是基于百度深度学习平台飞桨的一站式AI开发平台，提供在线编程环境、免费GPU算力、海量开源算法和开放数据，帮助开发者快速创建和部署模型。'
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