LLM2CLIP: Powerful Language Model Unlock Richer Visual Representation
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.
6731dc4921823ee4ea35fc5b
Weiquan Huang
6731dc4921823ee4ea35fc5c
Aoqi Wu
6731dc4921823ee4ea35fc5d
Yifan Yang
extracted_confirmed
2024-11-11T10:40:36.113Z
62d18eb81e36881a57f29bf4
/avatars/104851421b4ee9641daaf15942fa7ea1.svg
Yif Yang
user
Yif29
6731dc4921823ee4ea35fc5e
Xufang Luo
6731dc4921823ee4ea35fc5f
Yuqing Yang
6731dc4921823ee4ea35fc60
Liang Hu
6731dc4921823ee4ea35fc61
Qi Dai
6731dc4921823ee4ea35fc62
Xiyang Dai
6731dc4921823ee4ea35fc63
Dongdong Chen
6731dc4921823ee4ea35fc64
Chong Luo
6731dc4921823ee4ea35fc65
Lili Qiu
LLM2CLIP: Powerful Language Model Unlock Richer Visual Representation
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.
LLM2CLIP leverages large language models to enhance CLIP's multimodal representation learning, enabling better handling of complex image captions by fine-tuning the LLM and using contrastive learning.
CLIP multimodal foundational models rich supervision signals natural language cross-modal representation space large language models LLMs GPT-4 LLaMA language comprehension language generation textual understanding image captions contrastive learning caption space textual discriminability visual encoder context window