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cs.CV2026
Closing the Confusion Loop: CLIP-Guided Alignment for Source-Free Domain Adaptation
Shanshan Wang, Ziying Feng, Xiaozheng Shen +4
Source-Free Domain Adaptation (SFDA) tackles the problem of adapting a pre-trained source model to an unlabeled target domain without accessing any source data, which is quite suit…
cs.CV2024★ 1 cited
Dual-stream Feature Augmentation for Domain Generalization
Shanshan Wang, ALuSi, Xun Yang +3
Domain generalization (DG) task aims to learn a robust model from source domains that could handle the out-of-distribution (OOD) issue. In order to improve the generalization abili…
cs.CV2024
Gradually Vanishing Gap in Prototypical Network for Unsupervised Domain Adaptation
Shanshan Wang, Hao Zhou, Xun Yang +4
Unsupervised domain adaptation (UDA) is a critical problem for transfer learning, which aims to transfer the semantic information from labeled source domain to unlabeled target dom…