title: LLM2CLIP: Powerful Language Model Unlock Richer Visual Representation
summary: 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.
paper_authors._id: 6731dc4921823ee4ea35fc5b
paper_authors.name: Weiquan Huang
paper_authors._id: 6731dc4921823ee4ea35fc5c
paper_authors.name: Aoqi Wu
paper_authors._id: 6731dc4921823ee4ea35fc5d
paper_authors.name: Yifan Yang
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2024-11-11T10:40:36.113Z
paper_authors._id: 62d18eb81e36881a57f29bf4
paper_authors.avatarUrl: /avatars/104851421b4ee9641daaf15942fa7ea1.svg
paper_authors.fullname: Yif Yang
paper_authors.type: user
paper_authors.user: Yif29
paper_authors._id: 6731dc4921823ee4ea35fc5e
paper_authors.name: Xufang Luo
paper_authors._id: 6731dc4921823ee4ea35fc5f
paper_authors.name: Yuqing Yang
paper_authors._id: 6731dc4921823ee4ea35fc60
paper_authors.name: Liang Hu
paper_authors._id: 6731dc4921823ee4ea35fc61
paper_authors.name: Qi Dai
paper_authors._id: 6731dc4921823ee4ea35fc62
paper_authors.name: Xiyang Dai
paper_authors._id: 6731dc4921823ee4ea35fc63
paper_authors.name: Dongdong Chen
paper_authors._id: 6731dc4921823ee4ea35fc64
paper_authors.name: Chong Luo
paper_authors._id: 6731dc4921823ee4ea35fc65
paper_authors.name: Lili Qiu
paper_title: LLM2CLIP: Powerful Language Model Unlock Richer Visual Representation
paper_summary: 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.
paper_ai_keywords: multimodal foundational models CLIP natural language rich supervision signals large language models (LLMs) language comprehension language generation cross-modal representation space textual understanding image captions contrastive learning output embeddings textual discriminability visual encoder context window cross-modal tasks