IPL alternates discrete semantic token selection using approximate submodular optimization with continuous prompt optimization to boost both interpretability and task performance in vision-language model adaptation.
Tree of attributes prompt learning for vision-language models.arXiv preprint arXiv:2410.11201, 2024
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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.
citing papers explorer
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Joint Semantic Token Selection and Prompt Optimization for Interpretable Prompt Learning
IPL alternates discrete semantic token selection using approximate submodular optimization with continuous prompt optimization to boost both interpretability and task performance in vision-language model adaptation.
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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.