Tackling Ambiguity from Perspective of Uncertainty Inference and Affinity Diversification for Weakly Supervised Semantic Segmentation 
News
- Code will be public very soon Once UniA is accepted. 🔥🔥🔥
- Don't hesitate to give us a :star2: for updation!
- If you have any questions, please feel free to leave issues or contact us by zwyang21@m.fudan.edu.cn.
Overview
We proposed UniA, an unified single-stage framework, to tackle the ambiguity issue in WSSS.
Main Results
- Quantitative Results
| Semantic performance on VOC and COCO. Logs are available now. | Dataset | Backbone | Val | Test | Log | Weight |
|---|---|---|---|---|---|---|
| PASCAL VOC | ViT-B | 74.1 | 73.6 | log | weight | |
| MS COCO | ViT-B | 43.2 | - | log | weight |
- Qualitative Results
Citation
Please cite our work if you find it helpful to your reseach. :two_hearts:
@article{yang2024tackling,
title={Tackling Ambiguity from Perspective of Uncertainty Inference and Affinity Diversification for Weakly Supervised Semantic Segmentation},
author={Yang, Zhiwei and Meng, Yucong and Fu, Kexue and Wang, Shuo and Song, Zhijian},
journal={arXiv preprint arXiv:2404.08195},
year={2024}
}