Tackling Ambiguity from Perspective of Uncertainty Inference and Affinity Diversification for Weakly Supervised Semantic Segmentation ![arXiv](https://github.com/camo/9347ece4455d48d724e3ef6b98d93b7887743ba1984d94fe3447dce25be0b405/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f61725869762d323330332e30323530362d6233316231622e737667)
- Code will be public very soon Once UniA is accepted. 🔥🔥🔥
- Don't hesitate to give us a 🌟 for updation!
- If you have any questions, please feel free to leave issues or contact us by zwyang21@m.fudan.edu.cn.
We proposed UniA, an unified single-stage framework, to tackle the ambiguity issue in WSSS.
- 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
Please cite our work if you find it helpful to your reseach. 💕
@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}
}