LLM2CLIP: Powerful Language Model Unlock Richer Visual Representation

[2024-11-08] We are training a scaled-up version with ten times the dataset. Updates: EVA ViT-E, InternVL-300M, SigCLIP-SO-400M, and more VLLM results. Stay tuned for the most powerful CLIP models. Thanks for your star!
[2024-11-06] OpenAI's CLIP and EVA02's ViT models are now available on HuggingFace.
[2024-11-01] Our paper was accepted at the NeurIPS 2024 SSL Workshop!

Models & Code: https://aka.ms/llm2clip

Abstract
CLIP is one of the most important multimodal foundational models today, aligning visual and textual signals into a shared feature space using a simple contrastive learning loss on large-scale image-text pairs. 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. As a result, CLIP supports a variety of tasks, including zero-shot classification, detection, segmentation, and cross-modal retrieval, significantly influencing the entire multimodal domain.

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 text encoder’s context window and ability limitations. Our experiments demonstrate that this approach brings substantial improvements in cross-modal tasks.

LLM2CLIP Overview: After applying caption contrastive fine-tuning to the LLM, the increased textual discriminability enables more effective CLIP training. We leverage the open-world knowledge and general capabilities of the LLM to better process dense captions, addressing the previous limitations of the pretrained CLIP visual encoder and providing richer, higher-dimensional textual supervision.

Experimental Results
To validate our hypothesis, we designed a caption-to-caption retrieval experiment, as shown in Table 1 and Figure 2. Each image in the MS-COCO dataset has five human-annotated captions. We selected the first two captions as positive samples and performed retrieval across the entire validation set. Using the caption retrieval accuracy (CRA), we evaluated the text model’s ability to differentiate between captions, helping us determine which language model is better suited for CLIP. We found that Llama-3 8B achieved only 18.4% top-1 accuracy, while the standard CLIP-ViT-L reached 66.0% top-1 accuracy. As illustrated in Figure 2, the top-1 caption retrieved by original Llama-3 can be entirely unrelated to the query caption, clearly obstructing effective CLIP learning. Therefore, directly using an LLM to guide CLIP’s visual encoder training is highly constrained.