Ramen enables robust test-time adaptation of vision-language models under mixed-domain shifts by actively selecting domain-consistent and prediction-balanced samples via an embedding-gradient cache.
Tip- adapter: Training-free adaption of CLIP for few-shot classi- fication
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Ramen: Robust Test-Time Adaptation of Vision-Language Models with Active Sample Selection
Ramen enables robust test-time adaptation of vision-language models under mixed-domain shifts by actively selecting domain-consistent and prediction-balanced samples via an embedding-gradient cache.