Neural Architecture Search
1st lightweight NAS challenge and moving beyond
Track2: Performance Prediction Track
Track3: Dataset-Agnostic Track
JackRabbot Social Grouping and Activity Dataset and Benchmark
2nd Workshop on Visual Perception for Navigation in Human Environments
NTIRE 2021 challenges
New Trends in Image Restoration and Enhancement workshop and challenges on image and video processing
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
Recovery of Human Body Scans
Recovery of Generic Object Scans
Recovery of Feature Edges in 3D Object Scans
LOVEU: LOng-form VidEo Understanding
VizWiz Grand Challenge 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
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
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
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
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
International Challenge on Activity Recognition (ActivityNet)
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
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
Semantic and Instance Segmentation of building elements
Object Attribute Prediction of building elements
Learning from Limited or Imperfect Data
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
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
Adversarial Attacks on ML Defense Models
Unrestricted Adversarial Attacks on ImageNet
Continual Learning in Computer Vision
Robust Video Scene Understanding: Tracking and Video Segmentation
EarthVision: Large Scale Computer Vision for Remote Sensing Imagery
Image Matching: Local Features \& Beyond
Chart Question Answering Workshop
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
The Eight Workshop on Fine-Grained Visual Categorization
- 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
- Challenges for domain adaptation with varying levels of supervision.
- Challenges for semantic segmentation.