[17] Jaemin Na, Heechul Jung, Hyung Jin Chang, and Wonjun Hwang. Fixbi: Bridging domain spaces for unsupervised domain adaptation. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1094-1103, 2021.
[18] Ni Xiao and Lei Zhang. Dynamic weighted learning for unsupervised domain adaptation. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 15242-15251, 2021.
[19] Kuniaki Saito, Donghyun Kim, Stan Sclaroff, and Kate Saenko. Universal domain adaptation through self supervision. Advances in Neural Information Processing Systems, 33:16282-16292, 2020.
[20] Mengxue Li, Yi-Ming Zhai, You-Wei Luo, Peng-Fei Ge, and Chuan-Xian Ren. Enhanced transport distance for unsupervised domain adaptation. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 13936-13944, 2020.
[21] Shuhao Cui, Shuhui Wang, Junbao Zhuo, Chi Su, Qingming Huang, and Qi Tian. Gradually vanishing bridge for adversarial domain adaptation. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12455-12464, 2020.
[22] Fei Pan, Inkyu Shin, Francois Rameau, Seokju Lee, and In So Kweon. Unsupervised intra-domain adaptation for semantic segmentation through self-supervision. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3764-3773, 2020.
[23] Minghao Xu, Jian Zhang, Bingbing Ni, Teng Li, Chengjie Wang, Qi Tian, and Wenjun Zhang. Adversarial domain adaptation with domain mixup. In AAAI Conference on Artificial Intelligence, volume 34, pages 6502-6509, 2020.
[24] Jogendra Nath Kundu, Naveen Venkat, R Venkatesh Babu, et al. Universal source-free domain adaptation. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4544-4553, 2020.
[25] Han-Kai Hsu, Chun-Han Yao, Yi-Hsuan Tsai, Wei-Chih Hung, Hung-Yu Tseng, Maneesh Singh, and Ming-Hsuan Yang. Progressive domain adaptation for object detection. In IEEE/CVF Winter Conference on Applications of Computer Vision, pages 749-757, 2020.
[26] Yanchao Yang and Stefano Soatto. Fda: Fourier domain adaptation for semantic segmentation. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4085-4095, 2020.
[27] Yunsheng Li, Lu Yuan, and Nuno Vasconcelos. Bidirectional learning for domain adaptation of semantic segmentation. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6936-6945, 2019.
[28] Xinyi Wu, Zhenyao Wu, Yuhang Lu, Lili Ju, and Song Wang. Style mixing and patchwise prototypical matching for one-shot unsupervised domain adaptive semantic segmentation. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 36, pages 2740-2749, 2022.
[29] Takashi Isobe, Xu Jia, Shuaijun Chen, Jianzhong He, Yongjie Shi, Jianzhuang Liu, Huchuan Lu, and Shengjin Wang. Multi-target domain adaptation with collaborative consistency learning. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8187-8196, 2021.
[30] Antoine Saporta, Tuan-Hung Vu, Matthieu Cord, and Patrick Pérez. Multi-target adversarial frameworks for domain adaptation in semantic segmentation. In IEEE/CVF International Conference on Computer Vision, pages 9072-9081, 2021.
[31] Behnam Gholami, British Sahu, Ognjen Rudovic, Konstantinos Bousmalis, and Vladimir Pavlovic. Unsupervised multi-target domain adaptation: An information theoretic approach. IEEE Transactions on Image Processing, 29:3993-4002, 2020.
[32] Subhankar Roy, Evgeny Krivoshev, Zhun Zhong, Nicu Sebe, and Elisa Ricci. Curriculum graph co-teaching for multi-target domain adaptation. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5351-5360, 2021.
[33] Yusuke Iwasawa and Yutaka Matsuo. Test-time classifier adjustment module for model-agnostic domain generalization. Advances in Neural Information Processing Systems, 34:2427-2440, 2021.
[34] Chaoqi Chen, Jiongcheng Li, Xiaoguang Han, Xiaoqing Liu, and Yizhou Yu. Compound domain generalization via meta-knowledge encoding. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7119-7129, 2022.
[35] Xufeng Yao, Yang Bai, Xinyun Zhang, Yuechen Zhang, Qi Sun, Ran Chen, Ruiyu Li, and Bei Yu. Pcl: Proxy-based contrastive learning for domain generalization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7097-7107, 2022.