The DOI matches its Crossref record; 53 of the 55 references it cites were found in a registry or an index.
Mobile-Former: Bridging MobileNet and Transformer
Open accessAbstract
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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In the citing paper
“exceeds MobileNetV3 accuracy by 1.3 percentage points on the ImageNet dataset.”
From A Survey of Techniques for Optimizing Transformer Inference (2023) · cites this paper as [178]
In the citing paper
“The network also outperforms DETR [8] by 1.1 AP on the object detection task.”
From A Survey of Techniques for Optimizing Transformer Inference (2023) · cites this paper as [178]
In the citing paper
“The Mobile-former network [178] employs parallel MobileNetV2 and Transformer modules with twoway bridges (Mobile → Former and Mobile ← Former).”
From A Survey of Techniques for Optimizing Transformer Inference (2023) · cites this paper as [178]
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- touvron, 2020, Training data-efficient image transformers and distillation through at-tention, ArXiv Preprint
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Metadata
- 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)
- 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)
- 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
- 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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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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