CVPR 2021 竞赛汇总
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2021-03-08 06:32
极市导读
本文汇总了27个CVPR2021的竞赛并附有相关链接。 >>加入极市CV技术交流群,走在计算机视觉的最前沿
Neural Architecture Search
1st lightweight NAS challenge and moving beyond
https://www.cvpr21-nas.com/competition
早期的NAS方法通过将每个神经网络在训练数据上都训练到收敛,然后评估其效果,需要耗费大量的算力资源。
Track1:Supernet Track
赛道一为超网络赛道,旨在解决OneshotNAS的一致性问题;
Track2: Performance Prediction Track
赛道二为模型性能预测赛道,旨在不做任何训练的情况,准确的预测任意模型结构在特定评测集的性能。
Track3: Dataset-Agnostic Track
赛道三鼓励参与者提交与数据无关,但能够在完全未知的数据集上提供优秀结果的NAS算法。
JackRabbot Social Grouping and Activity Dataset and Benchmark
2nd Workshop on Visual Perception for Navigation in Human Environments
https://jrdb.stanford.edu/workshops/jrdb-cvpr21
除了JRDB上现有的四个基准和挑战(即2D-3D人员检测和跟踪挑战)之外,在本研讨会中,我们使用新的注释来组织两个新的挑战:
人类社会群体检测 个人动作检测和社交活动识别
NTIRE 2021 challenges
New Trends in Image Restoration and Enhancement workshop and challenges on image and video processing
https://data.vision.ee.ethz.ch/cvl/ntire21/
NTIRE Image challenges
Nonhomogeneous Dehazing Defocus Deblurring using Dual-pixel Depth Guided Image Relighting: Track 1 One-to-One relighting Depth Guided Image Relighting: Track 2 Any-to-Any relighting Perceptual Image Quality Assessment Image Deblurring: Track 1 Low Resolution Image Deblurring: Track 2 JPEG Artifacts Multi-Modal Aerial View Imagery Classification: Track 1 (SAR) Multi-Modal Aerial View Imagery Classification: Track 2 (SAR+EO) Learning the Super-Resolution Space
NTIRE video/multi-frame challenges
Quality enhancement of heavily compressed videos: Track 1 Fixed QP, Fidelity Quality enhancement of heavily compressed videos: Track 2 Fixed QP, Perceptual Quality enhancement of heavily compressed videos: Track 3 Fixed bit-rate, Fidelity Video Super-Resolution: Track 1 Spatial started! Video Super-Resolution: Track 2 Spatio-Temporal Burst Super-Resolution: Track 1 Synthetic Burst Super-Resolution: Track 2 Real High Dynamic Range (HDR): Track 1 Single frame High Dynamic Range (HDR): Track 2 Multiple frames
Mobile AI 2021 challenges
Learned ISP (MediaTek Dimensity APU platform) Image Denoising (Samsung Exynos Mali GPU platform) HDR Image Processing (Huawei Kirin Da Vinci NPU platform) Image Super-Resolution (Synaptics Dolphin NPU platform) Video Super-Resolution (OPPO Snapdragon Adreno GPU platform) Depth Estimation (Raspberry Pi 4 platform) Camera Scene Detection (Apple Bionic platform)
SHApe Recovery from Partial Textured 3D Scans
该研讨会的目的是推广在3D扫描处理中同时利用形状和纹理的概念,并特别注意从部分和嘈杂数据中恢复的特定任务。
https://cvi2.uni.lu/sharp2021/
Recovery of Human Body Scans
Recovery of Generic Object Scans
Recovery of Feature Edges in 3D Object Scans
LOVEU: LOng-form VidEo Understanding
https://sites.google.com/view/loveucvpr21/challenge
VizWiz Grand Challenge Workshop
https://vizwiz.org/workshops/2021-workshop/
Task: Image Captioning
Given an image, the task is to predict an accurate caption.
Task: Predict Answer to a Visual Question
Given an image and question about it, the task is to predict an accurate answer.
Task: Predict Answerability of a Visual Question
Given an image and question about it, the task is to predict if the visual question cannot be answered (with a confidence score in that prediction).
Bridging the Gap between Computational Photography and Visual Recognition
http://cvpr2021.ug2challenge.org/
TRACK 1: OBJECT DETECTION IN POOR VISIBILITY ENVIRONMENTS
TRACK 2: ACTION RECOGNITION FROM DARK VIDEOS
4th Workshop and Challenge on Learned Image Compression
image compression track
images need to be compressed to 0.075 bpp, 0.15 bpp, and 0.3 bpp (bits per pixel).
video compression track
short video clips need to be compressed to around 1 Mbit/s.
perceptual metric track
human preferences on pairs of images will have to be predicted. The image pairs will come from the decoders submitted to the image compression track.
5th AI City Challenge
https://www.aicitychallenge.org/
Challenge Track 1: Multi-Class Multi-Movement Vehicle Counting Using IoT Devices
Participating teams will count four-wheel vehicles and freight trucks that follow pre-defined movements from multiple camera scenes.
Challenge Track 2: City-Scale Multi-Camera Vehicle Re-Identification
Participating teams will perform vehicle re-identification based on vehicle crops from multiple cameras placed at multiple intersections.
Challenge Track 3: City-Scale Multi-Camera Vehicle Tracking
Participating teams will track vehicles across multiple cameras both at a single intersection and across multiple intersections spread out across a city.
Challenge Track 4: Traffic Anomaly Detection
Participating teams will submit all anomalies detected in the test data, including car crashes, stalled vehicles based on video feeds from multiple cameras at intersections and along highways.
Challenge Track 5: Natural Language-Based Vehicle Retrieval
Natural language (NL) description offers another useful way to specify vehicle track queries.
Large-scale Video Object Segmentation Challenge
https://youtube-vos.org/challenge/2021/
Our workshop has three challenges for different video segmentation tasks including semi-supervised video object segmentation, video instance segmentation and referring video object segmentation.
Track 1: Video Object Segmentation
Track 2: Video Instance Segmentation
Track 3: Referring Video Object Segmentation
Looking at People Large Scale Signer Independent Isolated SLR
http://chalearnlap.cvc.uab.es/challenge/43/description/
We are organizing a challenge on isolated sign language recognition from signer-independent non-controlled RGB-D data involving a large number of sign categories (>200).
RGB Competition Track
RGB+D Competition Track
3rd ScanNet Indoor Scene Understanding Challenge
http://www.scan-net.org/cvpr2021workshop/
International Challenge on Activity Recognition (ActivityNet)
http://activity-net.org/challenges/2021/
n this installment of the challenge, we will host seven guest tasks (tentative) focusing on different aspects of the activity recognition problem, especially expanding from online consumer video challenges to challenges on surveillance and first-person video.
Agriculture-Vision: Challenges & Opportunities for Computer Vision in Agriculture
https://www.agriculture-vision.com/
The 2nd Agriculture-Vision Prize Challenge aims to encourage research in developing novel and effective algorithms for agricultural pattern recognition from aerial images. Submissions will be evaluated and ranked by model performance.This year, we will be hosting two challenge tracks: supervised track and semi-supervised track. The top three performing submissions will receive prize rewards and presentation opportunities at our workshop.
Built Environment for the Design, Construction, and Operation of Buildings
https://cv4aec.github.io/
Semantic and Instance Segmentation of building elements
Object Attribute Prediction of building elements
Learning from Limited or Imperfect Data
https://l2id.github.io/
Learning from limited or imperfect data (L^2ID) refers to a variety of studies that attempt to address challenging pattern recognition tasks by learning from limited, weak, or noisy supervision.
Open World Vision
http://www.cs.cmu.edu/~shuk/open-world-vision.html#competition
Open-set image classification requires a model to distinguish novel, anomalous and semantically unknown (e.g., open-set) test-time examples.
Adversarial Machine Learning in Real-World Computer Vision Systems and Online Challenges
https://aisecure-workshop.github.io/amlcvpr2021/
Adversarial Attacks on ML Defense Models
Unrestricted Adversarial Attacks on ImageNet
Continual Learning in Computer Vision
https://eval.ai/web/challenges/challenge-page/829/overview
Robust Video Scene Understanding: Tracking and Video Segmentation
https://eval.vision.rwth-aachen.de/rvsu-workshop21/
EarthVision: Large Scale Computer Vision for Remote Sensing Imagery
http://www.classic.grss-ieee.org/earthvision2021/challenge.html
DynamicEarthNet Challenge
FloodNet Challenge
Image Matching: Local Features & Beyond
https://image-matching-workshop.github.io/
Chart Question Answering Workshop
https://cqaw.github.io/
The CQA challenge includes 3 levels of perception: from low-level visualization building blocks to semantic reasoning that requires text extraction.
2nd. Thermal Image Super-Resolution Challenge
https://pbvs-workshop.github.io/challenge.html
The Eight Workshop on Fine-Grained Visual Categorization
https://sites.google.com/view/fgvc8
GeoLifeCLEF2021 Semi-iNat2021 iNatChallenge2021 iMet2021 iMat-Fashion2021 Hotel-ID2021 HerbariumChallenge2021 iWildCam2021 Plant Pathology Challenge 2021
GAZE 2021 Challenges
The GAZE 2021 Challenges are hosted on Codalab, and can be found at:
ETH-XGaze Challenge: https://competitions.codalab.org/competitions/28930 EVE Challenge: https://competitions.codalab.org/competitions/28954
Autonomous Navigation in Unconstrained Environments
http://cvit.iiit.ac.in/autonue2021/challenge/
Challenges for domain adaptation with varying levels of supervision. Challenges for semantic segmentation.
其他链接
由于很多竞赛还在更新,完整竞赛参考CVPR官网:
http://cvpr2021.thecvf.com/workshops-schedule
https://github.com/skrish13/ml-contests-conf
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