Mysterious Projections: Multimodal LLMs Gain Domain-Specific Visual Capabilities Without Richer Cross-Modal Projections
[Paper]    [GitHub]

Gaurav Verma1, Minje Choi1, Kartik Sharma1,
Jamelle Watson-Daniels2, Sejoon Oh1, and Srijan Kumar1

1Georgia Institute of Technology, 2Harvard University
    





Overview of our study: While the MLLM's domain-specific visual capability can be improved using fine-tuning strategies, the domain-specific richness of the image's post-projection representation does not improve. Results indicate that domain-specific visual attributes are predominantly modeled by the LLM parameters (whether frozen or not) and the projection does not necessarily play a role in mapping visual attributes to the LLM space. Through this study, we offer a potential reinterpretation of the role of cross-modal projections in MLLMs.


Technical Abstract

Multimodal large language models (MLLMs) like LLaVA and GPT-4(V) enable general-purpose conversations about images with the language modality. As off-the-shelf MLLMs may have limited capabilities on images from domains like dermatology and agriculture, they must be fine-tuned to unlock domain-specific applications. The prevalent architecture of current open-source MLLMs comprises two major modules: an image-language (cross-modal) projection network and a large language model. It is desirable to understand the roles of these two modules in modeling domain-specific visual attributes to inform the design of future models and streamline the interpretability efforts on the current models. To this end, via experiments on $4$ datasets and under 2 fine-tuning settings, we find that as the MLLM is fine-tuned, it indeed gains domain-specific visual capabilities, but the updates do not lead to the projection extracting relevant domain-specific visual attributes. Our results indicate that the domain-specific visual attributes are modeled by the LLM, even when only the projection is fine-tuned. Through this study, we offer a potential reinterpretation of the role of cross-modal projections in MLLM architectures.

Annotated Key Results

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Paper and Bibtex

Mysterious Projections: Multimodal LLMs Gain Domain-Specific Visual Capabilities Without Richer Cross-Modal Projections
Gaurav Verma, Minje Choi, Kartik Sharma, Jamelle Watson-Daniels, Sejoon Oh, Srijan Kumar
arXiv preprint 2402.NNNNN

Webpage: https://claws-lab.github.io/projection-in-MLLMs
Code: https://github.com/claws-lab/projection-in-MLLMs
arXiv: https://arxiv.org/abs/2402.16832
Bibtex:

@article{verma2024mysterious,
title={Mysterious Projections: Multimodal LLMs Gain Domain-Specific Visual Capabilities Without Richer Cross-Modal Projections},
author={Verma, Gaurav and Choi, Minje and Sharma, Kartik and Watson-Daniels, Jamelle and Oh, Sejoon and Kumar, Srijan},
journal={arXiv preprint arXiv:2402.16832},
year={2024}
}


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