Towards Multimodal Understanding via Stable Diffusion as a Task-Aware
  Feature Extractor
Authors: name: Vatsal Agarwal, name: Matthew Gwilliam, name: Gefen Kohavi, name: Eshan Verma, name: Daniel Ulbricht, name: Abhinav Shrivastava

Recent advances in multimodal large language models (MLLMs) have enabled
image-based question-answering capabilities. However, a key limitation is the
use of CLIP as the visual encoder; while it can capture coarse global
information, it often can miss fine-grained details that are relevant to the
input query. To address these shortcomings, this work studies whether
pre-trained text-to-image diffusion models can serve as instruction-aware
visual encoders. Through an analysis of their internal representations, we find
diffusion features are both rich in semantics and can encode strong image-text
alignment. Moreover, we find that we can leverage text conditioning to focus
the model on regions relevant to the input question. We then investigate how to
align these features with large language models and uncover a leakage
phenomenon, where the LLM can inadvertently recover information from the
original diffusion prompt. We analyze the causes of this leakage and propose a
mitigation strategy. Based on these insights, we explore a simple fusion
strategy that utilizes both CLIP and conditional diffusion features. We
evaluate our approach on both general VQA and specialized MLLM benchmarks,
demonstrating the promise of diffusion models for visual understanding,
particularly in vision-centric tasks that require spatial and compositional
reasoning. Our project page can be found
https://vatsalag99.github.io/mustafar/.