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Computer Science > Computer Vision and Pattern Recognition

arXiv:2106.00666 (cs)
[Submitted on 1 Jun 2021 (v1), last revised 27 Oct 2021 (this version, v3)]

Title:You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection

Authors:Yuxin Fang, Bencheng Liao, Xinggang Wang, Jiemin Fang, Jiyang Qi, Rui Wu, Jianwei Niu, Wenyu Liu
View a PDF of the paper titled You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection, by Yuxin Fang and 7 other authors
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Abstract:Can Transformer perform 2D object- and region-level recognition from a pure sequence-to-sequence perspective with minimal knowledge about the 2D spatial structure? To answer this question, we present You Only Look at One Sequence (YOLOS), a series of object detection models based on the vanilla Vision Transformer with the fewest possible modifications, region priors, as well as inductive biases of the target task. We find that YOLOS pre-trained on the mid-sized ImageNet-1k dataset only can already achieve quite competitive performance on the challenging COCO object detection benchmark, e.g., YOLOS-Base directly adopted from BERT-Base architecture can obtain 42.0 box AP on COCO val. We also discuss the impacts as well as limitations of current pre-train schemes and model scaling strategies for Transformer in vision through YOLOS. Code and pre-trained models are available at this https URL.
Comments: NeurIPS 2021 Camera Ready
Subjects: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
Cite as: arXiv:2106.00666 [cs.CV]
  (or arXiv:2106.00666v3 [cs.CV] for this version)
  https://doi.org/10.48550/arXiv.2106.00666
arXiv-issued DOI via DataCite

Submission history

From: Yuxin Fang [view email]
[v1] Tue, 1 Jun 2021 17:54:09 UTC (2,914 KB)
[v2] Mon, 21 Jun 2021 02:28:30 UTC (16,431 KB)
[v3] Wed, 27 Oct 2021 02:14:12 UTC (16,435 KB)
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