This repository serves as a curated collection of paper readings and code implementations for advanced research in computer vision. It aims to help researchers and practitioners quickly grasp the essence of cutting-edge papers, making complex topics more accessible.

How It Works

The project breaks down complex research papers into digestible summaries, often accompanied by code reproductions. This approach facilitates understanding by providing both conceptual explanations and practical implementations, bridging the gap between theoretical knowledge and hands-on application.

Quick Start & Requirements

• Installation: Primarily involves cloning the repository and following individual paper's READMEs for specific setup.
• Dependencies: Varies by paper, but generally includes Python, PyTorch, and common CV libraries. Specific requirements like CUDA versions or datasets are detailed within each paper's documentation.
• Resources: Setup time and resource needs are paper-dependent, ranging from minimal for summaries to significant for code reproduction requiring GPUs.
• Links: FightingCV GitHub

Highlighted Details

• Comprehensive coverage of major computer vision conferences (CVPR, ICCV, ECCV, NeurIPS, ICLR, etc.) and journals (TPAMI).
• Includes summaries and code for a wide array of topics: object detection, segmentation, backbones, multi-modal learning, attention mechanisms, and more.
• Features a growing collection of code reproductions for recent and influential papers.
• Offers curated lists of papers on specific themes like "Attention," "MLP," and "Conv."

Maintenance & Community

• The project is actively maintained by xmu-xiaoma666, with community contributions encouraged.
• Links to WeChat groups and public accounts are provided for technical exchange and updates.

Licensing & Compatibility

• The repository itself does not specify a license. Individual code reproductions will adhere to their original paper's or repository's licensing. Users should verify licensing for any code they use.

Limitations & Caveats

The repository is a collection of summaries and code links, not a unified framework. Users must navigate individual paper setups, and the quality and completeness of code reproductions can vary.