FPL

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Fuzzy Positive Learning for Semi-supervised Semantic Segmentation (CVPR2023).

by Pengchong Qiao, Zhidan Wei, Yu Wang, Zhennan Wang, Guoli Song, Fan Xu, Xiangyang Ji, Chang Liu, Jie Chen

¹ School of Electronic and Computer Engineering, Peking University, ² Peng Cheng Laboratory, ³ Department of Automation and BNRist, Tsinghua University

Our FPL is a plug-and-play method that can be applied on commonly used SSL methods. Here we provide the codes based on CPS (CVPR2021) and AEL (NIPS2021). Many thanks for their great work.

Installation

Please refer to the Installation document.

Getting Started

Please refer to the Getting Started document.

Citation

Please consider citing this project in your publications if it helps your research.

@inproceedings{qiao2023fuzzy,
  title={Fuzzy Positive Learning for Semi-Supervised Semantic Segmentation},
  author={Qiao, Pengchong and Wei, Zhidan and Wang, Yu and Wang, Zhennan and Song, Guoli and Xu, Fan and Ji, Xiangyang and Liu, Chang and Chen, Jie},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={15465--15474},
  year={2023}
}