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Computer Science > Computer Vision and Pattern Recognition

arXiv:2309.16588 (cs)
[Submitted on 28 Sep 2023 (v1), last revised 12 Apr 2024 (this
version, v2)]

Title:Vision Transformers Need Registers

Authors:Timothee Darcet, Maxime Oquab, Julien Mairal, Piotr
Bojanowski
View a PDF of the paper titled Vision Transformers Need Registers, by
Timoth\'ee Darcet and 2 other authors
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    Abstract:Transformers have recently emerged as a powerful tool
    for learning visual representations. In this paper, we identify
    and characterize artifacts in feature maps of both supervised and
    self-supervised ViT networks. The artifacts correspond to
    high-norm tokens appearing during inference primarily in
    low-informative background areas of images, that are repurposed
    for internal computations. We propose a simple yet effective
    solution based on providing additional tokens to the input
    sequence of the Vision Transformer to fill that role. We show
    that this solution fixes that problem entirely for both
    supervised and self-supervised models, sets a new state of the
    art for self-supervised visual models on dense visual prediction
    tasks, enables object discovery methods with larger models, and
    most importantly leads to smoother feature maps and attention
    maps for downstream visual processing.

Subjects: Computer Vision and Pattern Recognition (cs.CV)
Cite as:  arXiv:2309.16588 [cs.CV]
          (or arXiv:2309.16588v2 [cs.CV] for this version)
          https://doi.org/10.48550/arXiv.2309.16588
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Timothee Darcet [view email]
[v1] Thu, 28 Sep 2023 16:45:46 UTC (5,243 KB)
[v2] Fri, 12 Apr 2024 09:38:33 UTC (6,803 KB)
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