This repository serves as a comprehensive, curated collection of research papers, datasets, and resources focused on camouflaged object detection (COD), segmentation (COS), and scene understanding (CSU). It targets researchers and engineers in computer vision, providing a centralized, up-to-date overview of the rapidly evolving field of camouflaged vision. The primary benefit is rapid access to cutting-edge methodologies and benchmarks, accelerating development and innovation in detecting objects that blend seamlessly with their surroundings.

How It Works

The repository functions as a dynamic knowledge base, meticulously organizing and categorizing a vast array of academic contributions. It systematically lists papers by publication year and venue, detailing model names, titles, and direct links to research papers and associated code repositories. The content is structured across various sub-disciplines, including general COD, video COD (VCOD), instance segmentation (CIS), weakly-supervised, semi-supervised, and zero-shot approaches, among others. This curated approach ensures users can efficiently navigate and discover relevant advancements in the field.

Quick Start & Requirements

As a curated list of research resources, this repository does not provide direct installation or execution instructions. Users are expected to access and implement the individual research papers and code repositories linked within the list. The primary requirement is access to academic literature and the technical capability to understand and utilize the cited methodologies.

Highlighted Details

• Extensive coverage of recent advancements, with regular updates reflecting papers from top-tier computer vision conferences (e.g., CVPR, ICCV, ECCV, AAAI, NeurIPS).
• Detailed categorization by specific sub-fields within camouflaged vision, such as video COD, instance segmentation, and various supervision paradigms.
• Direct links to papers and code for a wide range of models, facilitating practical exploration and reproduction.
• Inclusion of numerous datasets and benchmarks crucial for evaluating and comparing COD methods.

Maintenance & Community

The repository is committed to regular updates to maintain its comprehensiveness and relevance. While specific community channels or contributor details are not explicitly listed, the ongoing nature of updates suggests active maintenance by the repository owner.

Licensing & Compatibility

This repository itself is a collection of links and does not impose a specific license on the content it points to. Users must adhere to the licenses of the individual research papers and code repositories they access.

Limitations & Caveats

This is a curated list and not a deployable software library; users must independently retrieve and implement the cited research. The "Latest Updates" section includes future dates, indicating planned content rather than current availability. No direct executable code or pre-trained models are hosted within this repository.