MVSL improves low-resource biomedical image classification via decoupled vision-language adaptation, multi-granularity contrastive learning, and LLM-based semantic regularization.
A hard-to-beat baseline for training-free clip-based adaptation
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CLIP-SVD performs parameter-efficient adaptation of CLIP by fine-tuning singular values from SVD of weight matrices, reporting SOTA few-shot accuracy on 21 datasets plus a language-based interpretability analysis.
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Multi-View Synergistic Learning with Vision-Language Adaption for Low-Resource Biomedical Image Classification
MVSL improves low-resource biomedical image classification via decoupled vision-language adaptation, multi-granularity contrastive learning, and LLM-based semantic regularization.
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CLIP-SVD: Efficient and Interpretable Vision-Language Adaptation via Singular Values
CLIP-SVD performs parameter-efficient adaptation of CLIP by fine-tuning singular values from SVD of weight matrices, reporting SOTA few-shot accuracy on 21 datasets plus a language-based interpretability analysis.