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Project and Mix: Task-Semantic Prototypes for Few-Shot Image Classification
Vision-language models like CLIP are trained with the objective of aligning text and image pairs. Beyond text prompts alone, recent works show that exploiting few-shot image embeddings from a training set is effective for CLIP-based classification. In this work, we analyze mixing image and text prot
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