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microsoft · Nov 7, 2024 · e8b7ec4 · e8b7ec4
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-# LLM2CLIP: Extending the Capability Boundaries of CLIP through Large Language Models
+# LLM2CLIP: Powerful Language Model Unlock Richer Visual Representation
 ## Introduction
 
 LLM2CLIP aims to embrace the power of LLMs to unlock CLIP’s potential. By fine-tuning the LLM in the caption space using contrastive learning, LLM2CLIP extracts and amplifies textual capabilities into output embeddings. This process significantly enhances the textual discriminability of the output layer.
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 ## ❤️ Acknowlegement
 
-Our code is built on top of [eva-clip](https://github.com/baaivision/EVA/tree/master/EVA-CLIP). Thanks for their nice work!
+Our code is built on top of [eva-clip](https://github.com/baaivision/EVA/tree/master/EVA-CLIP). Thanks for their nice work!