CLIP is a seminal multimodal model that maps images and text into a shared representation space by contrastive learning on billions of image\u2013caption pairs. Inspired by the rapid progress of large language models (LLMs), we investigate how the superior linguistic understanding and broad world knowledge of LLMs can further strengthen CLIP\u2014particularly in handling long, complex captions. We introduce an efficient fine-tuning framework that embeds an LLM into a pretrained CLIP while incurring almost the same training cost as regular CLIP fine-tuning. Our method first \u201cembedding-izes\u201d the LLM for the CLIP setting, then couples it to the pretrained CLIP vision encoder through a lightweight adaptor trained on only a few million image\u2013caption pairs. With this strategy we achieve large performance gains\u2014without large-scale retraining\u2014over state-of-the-art CLIP variants such as EVA02 and SigLIP-2. The LLM-enhanced CLIP delivers consistent improvements across a wide spectrum of downstream tasks, including linear-probe classification, zero-shot image\u2013text retrieval with both short and long captions (in English and other languages), zero-shot\/supervised image segmentation, object detection, and used as tokenizer for multimodal large-model benchmarks.<\/p>\n","protected":false},"excerpt":{"rendered":"
CLIP is a seminal multimodal model that maps images and text into a shared representation space by contrastive learning on billions of image\u2013caption pairs. Inspired by the rapid progress of large language models (LLMs), we investigate how the superior linguistic understanding and broad world knowledge of LLMs can further strengthen CLIP\u2014particularly in handling long, complex […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":[{"type":"name","value":"Weiquan Huang","user_id":0},{"type":"name","value":"Aoqi Wu","user_id":0},{"type":"user_nicename","value":"Yifan Yang","user_id":"41539"},{"type":"user_nicename","value":"Xufang Luo","user_id":"40324"},{"type":"user_nicename","value":"Yuqing Yang","user_id":"40654"},{"type":"name","value":"Liang Hu","user_id":0},{"type":"user_nicename","value":"Qi Dai","user_id":"36689"},{"type":"user_nicename","value":"Chunyu Wang","user_id":"35609"},{"type":"user_nicename","value":"Xiyang 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