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Found 3 papers, 2 papers with code
CLIP-KD: An Empirical Study of Distilling CLIP Models
CLIP has become a promising language-supervised visual pre-training framework and achieves excellent performance over a wide range of tasks.
UPRISE: Universal Prompt Retrieval for Improving Zero-Shot Evaluation
Large Language Models (LLMs) are popular for their impressive abilities, but the need for model-specific fine-tuning or task-specific prompt engineering can hinder their generalization.
VL-Match: Enhancing Vision-Language Pretraining with Token-Level and Instance-Level Matching
Vision-Language Pretraining (VLP) has significantly improved the performance of various vision-language tasks with the matching of images and texts.
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