#Attention

Explore a meticulously curated repository focused on Vision Transformer and Attention, featuring comprehensive resources like papers, codes, and links to relevant websites. Maintained by Min-Hung Chen, this updated list invites contributions to enhance its comprehensiveness and includes the latest developments from major conferences such as NeurIPS 2023 and ICCV 2023. Researchers can contribute by opening issues or creating pull requests for any missed papers, ensuring a continually relevant resource for academic and enthusiast communities alike.

Discover the 'Attention is All You Need' Transformer model implemented in PyTorch, guided by a step-by-step YouTube tutorial. This resource offers clear insights into building and understanding transformer architectures effectively.

Generalizable NeRF Transformer (GNT) employs transformers to reconstruct Neural Radiance Fields efficiently. It utilizes a dual-stage architecture with attention mechanisms for scene representation and rendering, achieving results comparable to state-of-the-art methods. GNT demonstrates improvements in PSNR and capabilities in depth and occlusion inference, optimized for various datasets.