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Computer Science > Computer Vision and Pattern Recognition

arXiv:2203.09795 (cs)
[Submitted on 18 Mar 2022]

Title:Three things everyone should know about Vision Transformers

Authors:Hugo Touvron, Matthieu Cord, Alaaeldin El-Nouby, Jakob
Verbeek, Herve Jegou
View a PDF of the paper titled Three things everyone should know
about Vision Transformers, by Hugo Touvron and 4 other authors
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    Abstract:After their initial success in natural language
    processing, transformer architectures have rapidly gained
    traction in computer vision, providing state-of-the-art results
    for tasks such as image classification, detection, segmentation,
    and video analysis. We offer three insights based on simple and
    easy to implement variants of vision transformers. (1) The
    residual layers of vision transformers, which are usually
    processed sequentially, can to some extent be processed
    efficiently in parallel without noticeably affecting the
    accuracy. (2) Fine-tuning the weights of the attention layers is
    sufficient to adapt vision transformers to a higher resolution
    and to other classification tasks. This saves compute, reduces
    the peak memory consumption at fine-tuning time, and allows
    sharing the majority of weights across tasks. (3) Adding
    MLP-based patch pre-processing layers improves Bert-like
    self-supervised training based on patch masking. We evaluate the
    impact of these design choices using the ImageNet-1k dataset, and
    confirm our findings on the ImageNet-v2 test set. Transfer
    performance is measured across six smaller datasets.

Subjects: Computer Vision and Pattern Recognition (cs.CV)
Cite as:  arXiv:2203.09795 [cs.CV]
          (or arXiv:2203.09795v1 [cs.CV] for this version)
          https://doi.org/10.48550/arXiv.2203.09795
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Hugo Touvron [view email]
[v1] Fri, 18 Mar 2022 08:23:03 UTC (7,707 KB)
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