CoMaL: Conditional Maximum Likelihood Approach to Self-supervised Domain Adaptation in Long-tail Semantic Segmentation
This is an extended version of our BiMaL to address the long-tail semantic segmentation problem in the domain adaptation setting.
The implementation will be available soon.
If you find this code useful for your research, please consider citing:
[1] Truong, Thanh-Dat, Chi Nhan Duong, Ngan Le, Son Lam Phung, Chase Rainwater, and Khoa Luu. "Bimal: Bijective maximum likelihood approach to domain adaptation in semantic scene segmentation." In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 8548-8557. 2021.
[2] Truong, Thanh-Dat, Chi Nhan Duong, Pierce Helton, Ashley Dowling, Xin Li, and Khoa Luu. "CoMaL: Conditional Maximum Likelihood Approach to Self-supervised Domain Adaptation in Long-tail Semantic Segmentation", 2022.