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Do Text-Free Diffusion Models Learn Discriminative Visual Representations?

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Computer Vision – ECCV 2024 (ECCV 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15118))

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Abstract

Diffusion models have proven to be state-of-the-art methods for generative tasks. These models involve training a U-Net to iteratively predict and remove noise, and the resulting model can synthesize high-fidelity, diverse, novel images. However, text-free diffusion models have typically not been explored for discriminative tasks. In this work, we take a pre-trained unconditional diffusion model and analyze its features post hoc. We find that the intermediate feature maps of the pre-trained U-Net are diverse and have hidden discriminative representation properties. To unleash the potential of these latent properties of diffusion models, we present novel aggregation schemes. Firstly, we propose a novel attention mechanism for pooling feature maps and further leverage this mechanism as DifFormer, a transformer feature fusion of different diffusion U-Net blocks and noise steps. Next, we also develop DifFeed, a novel feedback mechanism tailored to diffusion. We find that diffusion models are better than GANs, and, with our fusion and feedback mechanisms, can compete with state-of-the-art representation learning methods for discriminative tasks – image classification with full and semi-supervision, transfer for fine-grained classification, object detection, and semantic segmentation. Our project website and code are available publicly.

S. Mukhopadhyay and M. Gwilliam—Equal contribution.

Y. Yamaguchi and V. Agarwal—Equal contribution.

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Acknowledgements

We thank Pulkit Kumar for his assistance with generating figures and polishing the manuscript. This work was partially supported by NSF CAREER Award (#2238769) to Abhinav Shrivastava. The authors acknowledge UMD’s supercomputing resources made available for conducting this research. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation thereon. Disclaimer: The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the NSF or the U.S. Government.

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Authors and Affiliations

  1. University of Maryland, College Park, MD, USA

    Soumik Mukhopadhyay, Matthew Gwilliam, Vatsal Agarwal, Namitha Padmanabhan, Archana Swaminathan, Tianyi Zhou & Abhinav Shrivastava

  2. Waseda University, Tokyo, Japan

    Yosuke Yamaguchi & Jun Ohya

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Editors and Affiliations

  1. University of Birmingham, Birmingham, UK

    Aleš Leonardis

  2. University of Trento, Trento, Italy

    Elisa Ricci

  3. Technical University of Darmstadt, Darmstadt, Germany

    Stefan Roth

  4. Princeton University, Princeton, NJ, USA

    Olga Russakovsky

  5. Czech Technical University in Prague, Prague, Czech Republic

    Torsten Sattler

  6. École des Ponts ParisTech, Marne-la-Vallée, France

    Gül Varol

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Mukhopadhyay, S. et al. (2025). Do Text-Free Diffusion Models Learn Discriminative Visual Representations?. In: Leonardis, A., Ricci, E., Roth, S., Russakovsky, O., Sattler, T., Varol, G. (eds) Computer Vision – ECCV 2024. ECCV 2024. Lecture Notes in Computer Science, vol 15118. Springer, Cham. https://doi.org/10.1007/978-3-031-73027-6_15

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