Model overview
LLM2CLIP: Extending the Capability Boundaries of CLIP through Large Language Models Weiquan Huang 1* , Aoqi Wu 1* , Yifan Yang 2† , Xufang Luo 2 , Yuqing Yang 2 , Liang Hu 1 , Qi Dai 2 , Xiyang Dai 2 , Dongdong Chen 2 , Chong Luo 2 , Lili Qiu 2 1 Tongji Universiy, 2 Microsoft Corporation * Equal contribution † Corresponding to: yifanyang@microsoft.com In this paper, we propose LLM2CLIP, a novel approach that embraces the power of LLMs to unlock CLIP’s potential. By fine-tuning the LLM in the caption space with contrastive learning, we extract its textual capabilities into the output embeddings, significantly improving the output layer’s textual discriminability. We then design an efficient training process where the fine-tuned LLM acts as a powerful teacher for CLIP’s visual encoder. Thanks to the LLM’s presence, we can now incorporate longer and more complex captions without being restricted by vanilla CLIP text encoder’s context window and ability limitations. Our experiments demonstrate that this approach brings substantial improvements in cross-modal tasks. Our method directly boosted the performance of the previously SOTA EVA02 model by 16.5% on both long-text and short-text retrieval tasks, transforming a CLIP model trained solely on English data into a state-of-the-art cross-lingual model. Moreover, when integrated into mul- timodal training with models like Llava 1.5, it consistently outperformed CLIP across nearly all benchmarks, demonstrating comprehensive performance improvements.
Key features
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Public benchmarks & run configuration
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Sources & traceability
许可证：apache-2.0。模型介绍与第三方评测均以原来源的具体版本和方法为准。