This collection serves as a comprehensive, curated resource for researchers and practitioners interested in Vision Transformers (ViTs). It aggregates a wide array of ViT variants and their applications across diverse computer vision tasks, providing a centralized point for exploring the rapidly evolving landscape of transformer-based image analysis. The primary benefit is a consolidated overview of state-of-the-art research and implementations, facilitating discovery and comparative analysis.

How It Works

This repository functions as a curated list of research papers and their associated code implementations, categorized by application domain. It does not present a unified framework but rather serves as an index to various ViT architectures and their adaptations for tasks such as image classification, object detection, segmentation, video processing, and multimodal applications. The approach is to systematically collect and organize links to relevant academic work, enabling users to discover and access specific ViT models and their implementations.

Quick Start & Requirements

This is a collection of links to research papers and code, not a runnable software package. Therefore, there is no "quick start" or installation process in the traditional sense. Requirements would depend entirely on the specific paper/code the user chooses to explore from the list.

Highlighted Details

• Breadth of Coverage: Encompasses a vast spectrum of ViT variants, including Swin Transformer, PVT, Mobile-ViT, DeiT, and many more.
• Task Diversity: Covers numerous downstream tasks: image backbone, point cloud processing, video analysis, model compression, transfer learning, detection, segmentation, pose estimation, tracking, generative models, self-supervised learning, robustness, and specialized domains like AI medicine and hardware co-design.
• Research Focus: Primarily links to academic papers and their corresponding code repositories, reflecting the cutting edge of ViT research.

Maintenance & Community

The repository is authored by Runwei Guan (University of Liverpool / JITRI-Institute of Deep Perception Technology). Information on active maintenance, community engagement (Discord/Slack), or specific contributors beyond the author is not detailed in the provided README snippet.

Licensing & Compatibility

The README snippet does not specify a license for the collection itself. The licensing of individual code repositories linked within the collection would vary and must be checked on a per-project basis.

Limitations & Caveats

This is a curated list of links, not a unified, installable library. Users must navigate to individual paper/code repositories to assess their specific requirements, dependencies, and licenses. The sheer volume of entries means it is a discovery tool rather than a direct implementation resource.