This repository serves as a comprehensive, curated list of papers, datasets, and implementations related to deep learning for semantic segmentation. It targets researchers and practitioners in computer vision and deep learning, providing a centralized resource for exploring state-of-the-art methods and foundational work in the field.

How It Works

The project functions as a knowledge base, meticulously cataloging academic papers, relevant datasets (e.g., VOC2012, CitySpaces, ADE20K), and code implementations across various deep learning frameworks. It categorizes resources by sub-fields like 3D segmentation, instance segmentation, weakly-supervised segmentation, and real-time applications, facilitating targeted exploration.

Quick Start & Requirements

This repository is a curated list and does not have a direct installation or execution command. Users are expected to follow links to individual paper implementations, which may require specific deep learning frameworks (e.g., TensorFlow, PyTorch, Caffe) and associated dependencies.

Highlighted Details

• Extensive coverage of papers from major conferences (CVPR, ICCV, ECCV) and arXiv.
• Categorization includes 2D, 3D, instance, weakly-supervised, and video semantic segmentation.
• Lists numerous datasets commonly used for benchmarking semantic segmentation tasks.
• Provides links to code implementations for many of the listed papers.

Maintenance & Community

The repository is maintained by tangzhenyu. It references external resources and communities like "really-awesome-semantic-segmentation" and "awesome-semantic-segmentation" for broader community engagement.

Licensing & Compatibility

The repository itself does not specify a license. Individual code implementations linked within the repository will have their own licenses, which users must adhere to. Compatibility depends entirely on the specific implementation being used.

Limitations & Caveats

This is a curated list, not a runnable framework. Users must independently find, set up, and manage the dependencies for each individual implementation. The sheer volume of resources may require significant effort to navigate and evaluate.