This repository serves as a comprehensive, up-to-date catalog of research papers exploring Vision Mamba architectures and their applications. It targets researchers and engineers interested in state-of-the-art computer vision models that offer an alternative to traditional Transformers, particularly for tasks involving long-range dependencies. The collection provides direct access to the latest advancements, enabling rapid evaluation of this emerging field.

How It Works

The core technology highlighted is Vision Mamba, an architectural paradigm that adapts State Space Models (SSMs) for visual tasks. Unlike Transformers, Vision Mamba leverages SSMs to efficiently model long sequences and spatial-temporal dependencies. This approach aims to provide a more scalable and performant alternative, particularly for high-resolution images or long video sequences, by offering linear or near-linear complexity with respect to sequence length, a significant advantage over the quadratic complexity of standard self-attention mechanisms.

Quick Start & Requirements

This repository is a curated list of academic papers and associated code links, not a deployable software project. Therefore, there are no installation instructions, dependencies, or setup requirements. Users are directed to individual research papers and their respective code repositories for implementation details.

Highlighted Details

• Extensive coverage of Vision Mamba research across diverse domains including remote sensing, medical imaging, video analysis, multimodal learning, and more.
• Provides direct links to arXiv pre-prints and, where available, associated code repositories for each listed paper.
• Acts as a dynamic, evolving resource for tracking the rapid progress in Vision Mamba architectures.

Maintenance & Community

Information regarding repository maintainers, community channels (e.g., Discord, Slack), or a project roadmap is not provided in the README. This appears to be a static, community-driven compilation of research.

Licensing & Compatibility

As a curated list of research papers, the repository itself does not have a software license. The linked papers are typically available under academic pre-print terms (e.g., arXiv) or specific research licenses. Compatibility for commercial use or integration into closed-source projects would depend on the licenses of the individual research papers and their associated code.

Limitations & Caveats

This resource is a survey of papers, not a functional library or framework. Users must independently access, understand, and implement the research from the linked sources. The rapid pace of development in this area means the list is constantly updated, and some papers may represent early-stage research or have unaddressed limitations.