Cited by 731Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot LearningVisual Attention and Saliency Detection

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Mobile-Former: Bridging MobileNet and Transformer

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Chen, Yinpeng, Dai, Xiyang, Chen, Dongdong, Liu, Mengchen, Dong, Xiaoyi, Yuan, Lu, Liu, Zicheng·2022·2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

DOI 10.1109/cvpr52688.2022.00520

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Abstract

We present Mobile-Former, a parallel design of MobileNet and transformer with a two-way bridge in between. This structure leverages the advantages of MobileNet at local processing and transformer at global interaction. And the bridge enables bidirectional fusion of local and global features. Different from recent works on vision transformer, the transformer in Mobile-Former contains very few tokens (e.g. 6 or fewer tokens) that are randomly initialized to learn global priors, resulting in low computational cost. Combining with the proposed light-weight cross attention to model the bridge, Mobile-Former is not only computationally efficient, but also has more representation power. It outperforms MobileNetV3 at low FLOP regime from 25M to 500M FLOPs on ImageNet classification. For instance, Mobile-Former achieves 77.9% top-1 accuracy at 294M FLOPs, gaining 1.3% over MobileNetV3 but saving 17% of computations. When transferring to object detection, Mobile-Former outperforms MobileNetV3 by 8.6 AP in RetinaNet framework. Furthermore, we build an efficient end-to-end detector by replacing backbone, encoder and decoder in DETR with Mobile-Former, which outperforms DETR by 1.3 AP but saves 52% of computational cost and 36% of parameters. Code will be released at https://github.com/aaboys/mobileformer.

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touvron, 2020, Training data-efficient image transformers and distillation through at-tention, ArXiv Preprint
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Authors: Chen, Y., Dai, X., Chen, D., Liu, M., Dong, X., Yuan, L., & Liu, Z.
Affiliations: Microsoft (United States) (US)
University of Science and Technology of China (CN)
ORCID: Chen, Yinpeng
Dai, Xiyang
Chen, Dongdong
Liu, Mengchen
Dong, Xiaoyi
Yuan, Lu
Liu, Zicheng
Venue: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Publisher: IEEE
Published: 2022-06
Pages: 5260-5269
ISSN: 1063-6919 (ISSN-L)
DOI: 10.1109/cvpr52688.2022.00520
OpenAlex: W3190492058
Cited by: 731
References: 55
Citations by year: 2026: 71 · 2025: 196 · 2024: 238 · 2023: 178 · 2022: 44
FWCI: 59.48
Type: conference paper
Language: en
Open access: green
arxiv.org
Licence: doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
Locations: IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
arXiv (Cornell University) (submittedVersion)
arXiv (Cornell University)
Topics: Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
Keywords: MobileNet, vision transformer, global-local feature fusion, cross-attention, DETR, RetinaNet, ImageNet classification

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Mobile-Former: Bridging MobileNet and Transformer

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