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AMixer: Adaptive Weight Mixing for Self-attention Free Vision Transformers

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Computer Vision – ECCV 2022 (ECCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13681))

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Abstract

Vision Transformers have shown state-of-the-art results for various visual recognition tasks. The dot-product self-attention mechanism that replaces convolution to mix spatial information is commonly recognized as the indispensable ingredient behind the success of vision Transformers. In this paper, we thoroughly investigate the key differences between vision Transformers and recent all-MLP models. Our empirical results show the superiority of vision Transformers mainly comes from the data-dependent token mixing strategy and the multi-head scheme instead of query-key interactions. Inspired by this observation, we propose a computationally and parametrically efficient operation named adaptive weight mixing to generate attention weights without token-token interactions. Based on this operation, we develop a new architecture named as AMixer to capture both long-term and short-term spatial dependencies without self-attention. Extensive experiments demonstrate that our adaptive weight mixing is more efficient and effective than previous weight generation methods and our AMixer can achieve a better trade-off between accuracy and complexity than vision Transformers and MLP models on both ImageNet and downstream tasks. Code is available at https://github.com/raoyongming/AMixer.

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Acknowledgment

This work was supported in part by the National Key Research and Development Program of China under Grant 2017YFA0700802, in part by the National Natural Science Foundation of China under Grant 62125603 and Grant U1813218, in part by a grant from the Beijing Academy of Artificial Intelligence (BAAI).

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Authors and Affiliations

  1. Department of Automation, Tsinghua University, Beijing, China

    Yongming Rao, Wenliang Zhao, Jie Zhou & Jiwen Lu

  2. Beijing National Research Center for Information Science and Technology, Beijing, China

    Yongming Rao, Wenliang Zhao, Jie Zhou & Jiwen Lu

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  1. Yongming Rao
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  2. Wenliang Zhao
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  3. Jie Zhou
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  4. Jiwen Lu
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Correspondence to Jiwen Lu .

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Editors and Affiliations

  1. Tel Aviv University, Tel Aviv, Israel

    Shai Avidan

  2. University College London, London, UK

    Gabriel Brostow

  3. Google AI, Accra, Ghana

    Moustapha Cissé

  4. University of Catania, Catania, Italy

    Giovanni Maria Farinella

  5. Facebook (United States), Menlo Park, CA, USA

    Tal Hassner

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Rao, Y., Zhao, W., Zhou, J., Lu, J. (2022). AMixer: Adaptive Weight Mixing for Self-attention Free Vision Transformers. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13681. Springer, Cham. https://doi.org/10.1007/978-3-031-19803-8_4

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  • DOI: https://doi.org/10.1007/978-3-031-19803-8_4

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