EntitySeg Toolbox: Towards open-world and high-quality image segmentation

EntitySeg is an open source toolbox which towards open-world and high-quality image segmentation. All works related to image segmentation from our group are open-sourced here.

To date, EntitySeg implements the following algorthms:

• Open-World Entity Segmentation (TAPMI2022) --- released
• High Quality Segmentation for Ultra High-resolution Images (CVPR2022) --- released
• CA-SSL: Class-Agnostic Semi-Supervised Learning for Detection and Segmentation (ECCV2022) --- code to be released
• Fine-Grained Entity Segmentation --- paper, dataset and code to be released
• Automatically Discovering Novel Visual Categories with Adaptive Prototype Learning --- code to be released

Usage

Please refer to the README.md of each project. All projects would be merged to support each other in the soon.

Citing Ours

@article{qi2021open,
  title={Open-World Entity Segmentation},
  author={Qi, Lu and Kuen, Jason and Wang, Yi and Gu, Jiuxiang and Zhao, Hengshuang and Lin, Zhe and Torr, Philip and Jia, Jiaya},
  journal={arXiv preprint arXiv:2107.14228},
  year={2021}
}

@article{shen2021high,
  title={High Quality Segmentation for Ultra High-resolution Images},
  author={Tiancheng Shen, Yuechen Zhang, Lu Qi, Jason Kuen, Xingyu Xie, Jianlong Wu, Zhe Lin, Jiaya Jia},
  journal={CVPR},
  year={2022}
}

@article{qi2022cassl,
  title={CA-SSL: Class-Agnostic Semi-Supervised Learning for Detection and Segmentation},
  author={Qi, Lu and Kuen, Jason and Lin, Zhe and Gu, Jiuxiang and Rao, Fengyun and Li, Dian and Guo, Weidong and Wen, Zhen and Yang, Ming-Hsuan and Jia, Jiaya},
  journal={ECCV},
  year={2022}
}