1 citations · 1 across the 2 of their papers we have counts for
2 papers
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…