Summary

This repository serves as a curated collection of state-of-the-art research papers on Retrieval-Augmented Generation (RAG) applied to Computer Vision. It targets researchers and practitioners seeking to understand and leverage RAG for advanced visual tasks, offering a centralized resource for cutting-edge advancements in image/video understanding and generation.

How It Works

Retrieval-Augmented Generation (RAG) in Computer Vision integrates retrieval modules into generative models, enabling them to query external knowledge bases during inference. This approach enriches models with additional context, leading to improved performance and interpretability across various vision tasks. Applications detailed in the repository include image captioning and object detection enhanced by external knowledge, video QA/comprehension using long transcripts or references, and visual generation leveraging retrieved reference images or domain-specific data.

Quick Start & Requirements

This repository is a curated list of research papers and resources, not a software project with installation instructions.

Highlighted Details

• Broad Application Spectrum: Covers RAG's impact on visual understanding (image/video description, object detection, spatial understanding, medical vision), visual generation (2D, 3D, video), and embodied AI (autonomous driving, navigation).
• Extensive Paper Catalog: Features a comprehensive and categorized list of recent research papers (2022-2025) from top conferences (CVPR, ICLR, NIPS, ECCV, ACL, etc.) and pre-print servers, facilitating a deep dive into specific sub-fields.
• Practical Resources: Offers a dedicated "Resources" section with links to workshops, tutorials, and guides on building multimodal RAG systems for diverse applications like image search, video interaction, and document analysis.
• Emerging Trends: Highlights forward-looking research, with many papers projected for 2025, indicating active and evolving research frontiers in multimodal RAG.

Maintenance & Community

The project is community-driven, encouraging contributions of new papers via Pull Requests. Specific details on maintainers, active development, or community channels (e.g., Discord, Slack) are not provided in the README.

Licensing & Compatibility

No licensing information is specified in the provided README content.

Limitations & Caveats

As a curated list, this repository does not present a software system with inherent limitations. It focuses on cataloging existing research and does not detail specific challenges or unsupported platforms related to RAG implementation in computer vision.