#Research Papers

The project comprises daily updates on recent studies in computer vision, AI, and related disciplines, compiling a comprehensive repository of high-caliber papers from renowned conferences such as CVPR, IJCAI, and ICLR. The CVer community offers opportunities to explore advancements in AI applications across areas like object detection, semantic segmentation, GAN, and NeRF. Discover a multitude of studies to remain informed on cutting-edge computer vision and AI developments.

Discover a curated compilation of research papers on self-supervised learning in graph neural networks (GNNs), organized by year. This resource includes widely-cited studies and offers access to related code for further exploration. Stay informed on recent developments, suggest additions, or report errors. Topics covered include community detection, representation learning, and anomaly detection, with insights into pioneering techniques and applications.

Access an extensive selection of research papers from CVPR 2024, showcasing the forefront of computer vision and deep learning. The repository offers code implementations to explore advancements in visual intelligence. Stay informed on the latest developments in image synthesis, 3D modeling, and more from top researchers worldwide.

This repository features a detailed collection of ICCV conference papers and code resources from 1987 to 2023. It provides organized summaries of significant ICCV papers, complete with download options for ease of access. Readers can explore current interpretations and presentations of ICCV2023 papers, with ongoing updates for fresh insights. The repository also includes categorized summaries and links for ICCV2021 and ICCV2019, ensuring comprehensive access to papers and code for researchers and enthusiasts. Regular updates make this resource essential for exploring the evolution of computer vision advancements at ICCV.

Explore a diverse selection of Colab notebooks designed for machine learning tasks. This repository includes notable projects such as Segment Anything 2 and Open-Unmix, showcasing applications from visual segmentation to music source separation. Regular updates ensure a comprehensive resource for developers and researchers to explore cutting-edge machine learning models and methodologies. Suitable for academic and practical use, it links directly to GitHub resources and research publications for seamless exploration.

Delve into an extensive library of annotated research papers meant for easier comprehension, primarily aimed at machine learning professionals. This effort strives to demystify complex research through concise annotations and insightful analysis. Featuring a curated collection of significant papers across fields like Computer Vision, NLP, and Diffusion Models, this resource supports professionals in staying current with industry advancements and enriching their learning journey.

Explore a meta-repository for semantic search and similarity, with collections of research papers, articles, libraries, tools, and datasets. Contribute by raising a PR to expand this knowledge base, designed for those interested in semantic search.

This survey offers an expansive view of recent developments in large language models (LLMs) within artificial intelligence. It aggregates a significant number of research papers from diverse conferences and open-access resources, providing an extensive review of the LLM field. Topics covered include alignment, data management, societal implications, and applications in sectors like healthcare and education. Key challenges such as safety, misinformation, and efficiency are also examined. This compilation is intended to assist researchers and practitioners in gaining a rapid understanding of the field and in exploring emerging research pathways in LLMs.