#ECCV2024

Discover a collection of key papers and codes for CVPR2024 and ECCV2024 dedicated to AI-generated content (AIGC). This repository is a useful resource for researchers and interested parties in computer vision, providing insights into recent developments. Contributions in the form of stars, forks, or pull requests are welcome. Explore research groups and vision studies to stay updated with the latest trends.

BrushNet is a diffusion-based image inpainting model that integrates easily with pre-trained systems. Its dual-branch diffusion approach effectively addresses image completion tasks and enhances control by separating masked image features and noisy latent spaces. The model offers adaptable training and inference capabilities, with detailed deployment instructions. Recognized for its innovative design, BrushNet won top prizes at the CVPR2024 GenAI Media Generation Challenge.

Long-CLIP expands CLIP's text input limit to 248 characters, enhancing performance in long-caption text-image retrieval with a 20% increase in R@5 metric. It also improves traditional text-image retrieval by 6%. This solution is versatile for developers and researchers needing long-text processing. Recently updated with model checkpoints and evaluation codes, the project is featured in ECCV2024, providing insights for SDXL and Urban-1k dataset enhancement.

The project 'Pixel-Aware Stable Diffusion' aims to advance image super-resolution and stylization by employing stable diffusion techniques. Featuring the PASD-SDXL update, it offers enhancements in image quality and operational stability. With capabilities such as colorization and photo restoration, the project is accessible via Huggingface and Colab. Resources provided assist developers through thorough installation guides and timely updates, making it an ideal choice for AI-based image advancements. The focus remains on objectively delivering improved visual outcomes using cutting-edge technology.

Explore this well-structured repository of key papers and codes from ECCV2024 and ECCV2020 that address various low-level vision tasks such as super-resolution, image de-raining, dehazing, and more. The collection supports the research community by encouraging contributions through stars, forks, and PRs, making it a valuable tool for advancing computer vision studies without biased promotion.