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PuRF is introduced, a novel PuRiFication-driven cache-based method for multi-label test-time adaptation of vision-language models that consistently outperforms state-of-the-art methods on ViT-B/32 across five datasets.
This comprehensive survey formally defines the CTTA problem, analyzes the diverse continual domain shift patterns that characterize different evaluation protocols, and proposes a hierarchical taxonomy that categorizes existing methods into three families: optimization-based strategies (entropy minimization, pseudo-labeling, parameter restoration), parameter-efficient methods (normalization layer adaptation, adaptive parameter selection), and architecture-based approaches (teacher-student frameworks, adapters, visual prompting, masked modeling).