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cs.CV2026★ 1 cited
Domain-Division based Progressive Learning for Source-Free Domain Adaptation
Pan Liu, Jing Li, Meng Zhao +3
With growing privacy and portability concerns, source-free domain adaptation requires only a source pre-trained model and an unlabeled target domain, allowing for effective adaptat…
cs.CV2026
LFM: Leveraging Foundation Models for Source-Free Universal Domain Adaptation
Jing Li, Pan Liu, Meng Zhao +7
Source-free universal domain adaptation (SF-UniDA) adapts a pre-trained source model to an unlabeled target domain under both covariate and label shifts, without access to source d…
cs.CV2025
When Confidence Fails: Revisiting Pseudo-Label Selection in Semi-supervised Semantic Segmentation
Pan Liu, Jinshi Liu
While significant advances exist in pseudo-label generation for semi-supervised semantic segmentation, pseudo-label selection remains understudied. Existing methods typically use f…