LLM2CLIP: Powerful Language Model Unlocks Richer Cross-Modality Representation

Understand

CLIP is a seminal multimodal model that maps images and text into a shared representation space through contrastive learning on billions of image-caption 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, particularly in handling long and complex captions.
• We introduce an efficient fine-tuning framework that embeds an LLM into a pretrained CLIP while incurring nearly the same training cost as standard CLIP fine-tuning.
• Our method first converts the LLM into an embedding-compatible form for the CLIP setting, and then couples it with the pretrained CLIP vision encoder through a lightweight adaptor trained on only a few million image-caption pairs.