This repository serves as a curated collection of papers and their associated code from the CVPR 2022 conference, focusing on computer vision research. It categorizes papers across 36 subfields, including object detection, segmentation, image processing, 3D vision, and various learning paradigms, providing a valuable resource for researchers and practitioners seeking state-of-the-art methods and implementations.

How It Works

The repository organizes papers by research topic, listing the paper title, a brief description, and direct links to the paper PDF and its corresponding code repository. This structure allows users to quickly navigate and access relevant research, facilitating the exploration of new techniques and the reproduction of experimental results.

Quick Start & Requirements

• Access: Browse the categorized list of papers and code directly from the README.
• Requirements: Each linked code repository will have its own specific dependencies, typically involving Python, deep learning frameworks (PyTorch, TensorFlow), and potentially specialized libraries for computer vision tasks.

Highlighted Details

• Comprehensive coverage of CVPR 2022 computer vision research.
• Categorization across 36 distinct subfields.
• Direct links to papers and code for easy access and reproduction.
• Useful for staying updated on the latest advancements in computer vision.

Maintenance & Community

This is a static collection of links, with maintenance likely dependent on the original authors of the linked papers and code. Community interaction would primarily occur within the individual code repositories or related academic forums.

Licensing & Compatibility

The licensing of the papers and code is determined by their respective authors and repositories. Users must adhere to the licenses of each individual project they utilize.

Limitations & Caveats

This repository is a directory and does not host any code itself. The availability and functionality of the linked code depend entirely on the original authors. Some links may become outdated or broken over time.