CVPR 2021 竞赛汇总

技术讨论 hello_uncle ⋅ 于 1个月前 ⋅ 559 阅读

作者丨Coggle
来源丨Coggle数据科学
编辑丨极市平台

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:

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:

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