LLM2CLIP: Powerful Language Model Unlock Richer Visual Representation
Weiquan Huang, Aoqi Wu, Yifan Yang, Xufang Luo, Yuqing Yang, Liang Hu, Qi Dai, Xiyang Dai, Dongdong Chen, Chong Luo, Lili Qiu
2024-11-11
Summary
This paper discusses LLM2CLIP, a new method that combines large language models (LLMs) with the CLIP model to improve how machines understand and generate visual content based on text.
What's the problem?
The existing CLIP model is powerful for connecting images and text, but it struggles with understanding complex or long descriptions. This limitation affects its ability to perform well in tasks that require detailed visual comprehension, making it less effective for advanced applications.
What's the solution?
LLM2CLIP enhances CLIP by integrating a large language model that has strong text understanding. The researchers fine-tune this LLM specifically for handling captions, allowing it to produce better output when paired with CLIP. This new approach enables CLIP to process longer and more complicated text without being limited by its previous capabilities. The fine-tuned LLM acts as a 'teacher' for CLIP, improving its overall performance in visual tasks.
Why it matters?
This research is significant because it demonstrates how combining advanced language models with visual systems can lead to better AI that understands images and text together. By improving CLIP's ability to handle complex descriptions, LLM2CLIP can enhance applications in areas like image captioning, visual search, and cross-lingual tasks, making AI tools more effective for users.
Abstract
CLIP is one of the most important multimodal foundational models today. What powers CLIP's capabilities? The rich supervision signals provided by natural language, the carrier of human knowledge, shape a powerful cross-modal representation space. However, with the rapid advancements in large language models LLMs like GPT-4 and LLaMA, the boundaries of language comprehension and generation are continually being pushed. This raises an intriguing question: can the capabilities of LLMs be harnessed to further improve multimodal representation learning? The potential benefits of incorporating LLMs into CLIP are clear. LLMs' strong textual understanding can fundamentally improve CLIP's ability to handle image captions, drastically enhancing its ability to process long and complex texts, a well-known limitation of vanilla CLIP. Moreover, LLMs are trained on a vast corpus of text, possessing open-world knowledge. This allows them to expand on caption information during training, increasing the efficiency of the learning process. 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's text encoder's context window and ability limitations. Our experiments demonstrate that this approach brings substantial improvements in cross-modal tasks.