LFM: Leveraging Foundation Models for Source-Free Universal Domain Adaptation
This framework adapts a pre-trained model to an unlabeled target domain without source data by using foundation models (LFM). It uses a vision-language model to generate pseudo-labels and determines label shift types via similarity scores. A consensus strategy refines these labels using the target model initialized with the source knowledge, leading to superior adaptation performance across various label shifts.