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Found 3 papers, 1 papers with code
CLIPArTT: Light-weight Adaptation of CLIP to New Domains at Test Time
In this study, we introduce CLIP Adaptation duRing Test-Time (CLIPArTT), a fully test-time adaptation (TTA) approach for CLIP, which involves automatic text prompts construction during inference for their use as text supervision.
NC-TTT: A Noise Contrastive Approach for Test-Time Training
Despite their exceptional performance in vision tasks, deep learning models often struggle when faced with domain shifts during testing.
Revisiting Learnable Affines for Batch Norm in Few-Shot Transfer Learning
Batch Normalization is a staple of computer vision models, including those employed in few-shot learning.
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