Introduction to Ultimate-Awesome-Transformer-Attention
The Ultimate-Awesome-Transformer-Attention project is a meticulously curated repository, spearheaded by Min-Hung Chen, dedicated to collecting and maintaining a comprehensive list of resources centered around Vision Transformer and Attention mechanisms. This project is a valuable resource for researchers, practitioners, and anyone interested in the cutting-edge advancements in the field of neural networks, especially the transformative impact of Vision Transformers.
Repository Highlights
This repository includes a rich collection of papers, code, and relevant websites all focused on Vision Transformers and Attention. The repository is regularly updated to include the latest advancements and contributions from major conferences. As of the latest updates, it now includes papers from NeurIPS 2023 and ICCV 2023, demonstrating its commitment to staying current with the fast-paced developments in this field.
Community and Contributions
The project is highly collaborative, encouraging contributions from the community. If visitors notice any overlooked papers, they are not only welcome but encouraged to create pull requests, open issues, or even reach out directly via email. This openness ensures the repository remains comprehensive and beneficial to a wider audience.
Structure and Content
The repository is organized into several key sections, reflecting the vast scope of Vision Transformers and Attention. These include:
- Image Classification / Backbone: Delve into how convolutional layers can be replaced or enhanced with attention mechanisms or explore the realm of Vision Transformers, including general and efficient types.
- Detection and Segmentation: This section encompasses a variety of detection tasks including object and 3D object detection, as well as segmentation tasks like semantic segmentation and depth estimation.
- Video Processing: It covers high-level video tasks such as action recognition, detection, and video object segmentation.
- Multi-Modality: The repository dives into tasks that require understanding multiple modalities like visual captioning and visual question answering.
- Other Vision Tasks: The repository also explores other significant areas like pose estimation, tracking, and reinforcement learning, showcasing the versatility and wide applicability of transformers.
Surveys
A distinctive feature of the repository is its survey section. This section provides curated lists and insights about the current trends and future directions in multi-modal large language models, video understanding, and other related areas. These surveys offer an excellent starting point for anyone entering the field or looking to update themselves with the latest research trends.
Conclusion
The Ultimate-Awesome-Transformer-Attention repository serves as an indispensable tool for those engaged in the research and application of Vision Transformers and Attention mechanisms. By providing a centralized, up-to-date resource, it supports easier access to information and fosters a vibrant community of innovators contributing to the exciting developments in this area. Anyone interested in the field is encouraged to visit the repository, explore its offerings, and contribute to its growth.