Papers › LLM2CLIP: Powerful Language Model Unlocks Richer Visual Representation

LLM2CLIP: Powerful Language Model Unlocks Richer Visual Representation

7 Nov 2024arXiv:2411.04997archive 2025-07-28

Weiquan Huang, Aoqi Wu, Yifan Yang, Xufang Luo, Yuqing Yang, Liang Hu, Qi Dai, Chunyu Wang, Xiyang Dai, Dongdong Chen, Chong Luo, Lili Qiu

CLIP is a foundational multimodal model that aligns image and text features into a shared representation space via contrastive learning on large-scale image-text pairs. Its effectiveness primarily stems from the use of natural language as rich supervision. Motivated by the remarkable advancements in large language models (LLMs), this work explores how LLMs' superior text understanding and extensive open-world knowledge can enhance CLIP's capability, especially for processing longer and more complex image captions. We propose an efficient post-training strategy that integrates LLMs into pretrained CLIP. To address the challenge posed by the autoregressive nature of LLMs, we introduce a caption-to-caption contrastive fine-tuning framework, significantly enhancing the discriminative quality of LLM outputs. Extensive experiments demonstrate that our approach outperforms LoRA-based methods, achieving nearly fourfold faster training with superior performance. Furthermore, we validate substantial improvements over state-of-the-art models such as CLIP, EVA02, and SigLip2 across various zero-shot multimodal retrieval tasks, cross-lingual retrieval tasks, and multimodal language model pretraining.

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microsoft/LLM2CLIP officialmentioned on GitHubpytorch report

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Tasks

Contrastive Learning Image Captioning Language Modeling Language Modelling Representation Learning Retrieval World Knowledge model

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Methods

Absolute Position Encodings Adam Attention BPE CLIP Contrastive Learning Dense Connections Dropout GPT-4 LLaMA Label Smoothing Layer Normalization Linear Layer Multi-Head Attention Position-Wise Feed-Forward Layer Residual Connection Softmax Transformer

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