GA2-CLIP uses generic attribute anchors and coupled hard-soft prompts to preserve generalization in prompt-tuned video-language models on base-to-new class tasks.
Maple: Multi-modal prompt learning
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MVSL improves low-resource biomedical image classification via decoupled vision-language adaptation, multi-granularity contrastive learning, and LLM-based semantic regularization.
citing papers explorer
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GA2-CLIP: Generic Attribute Anchor for Efficient Prompt Tuningin Video-Language Models
GA2-CLIP uses generic attribute anchors and coupled hard-soft prompts to preserve generalization in prompt-tuned video-language models on base-to-new class tasks.
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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.