This repository serves as a curated digest of research papers focusing on Transformer-based approaches in visual tracking tasks, specifically Unified Tracking (UT), Single Object Tracking (SOT), and 3D Single Object Tracking (3DSOT). It aims to provide researchers and practitioners with a comprehensive overview of the latest advancements and trends in this rapidly evolving field.

How It Works

The repository organizes papers by tracking task and publication year, listing key details such as paper titles, links to official papers, and code repositories where available. It highlights recent trends like autoregressive temporal modeling and joint feature extraction/interaction, emphasizing the benefits of leveraging pre-trained Vision Transformer models for faster inference and improved tracking performance.

Quick Start & Requirements

This repository is a paper digest and does not have direct installation or execution commands. It requires a web browser to access the listed papers and code.

Highlighted Details

• Comprehensive coverage of Transformer-based tracking methods across UT, SOT, and 3DSOT.
• Links to papers and code for easy access to implementations and detailed methodologies.
• Identification of recent trends and architectural advancements in Transformer tracking.
• Inclusion of helpful learning resources like surveys and talks on visual tracking.

Maintenance & Community

The repository is maintained by Little-Podi. Further community engagement or roadmap details are not explicitly provided in the README.

Licensing & Compatibility

The licensing of the individual papers and code repositories linked within this digest varies. Users should refer to the specific licenses of each linked resource for compatibility and usage terms.

Limitations & Caveats

This repository is a digest and does not provide executable code or benchmarks itself. The availability and functionality of linked code repositories are subject to their respective maintainers. Some entries indicate missing code or papers.