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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
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]
Citations
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333
Microsoft COCO: Common Objects in Context
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., & Zitnick, C. L. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10602-1_48 · Cited by 45,460
Squeeze-and-Excitation Networks
Hu, J., Shen, L., & Sun, G. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00745 · Cited by 35,667
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., & Guo, B. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00986 · Cited by 35,534
Self-citationFeature Pyramid Networks for Object Detection
Lin, T.-Y., Dollar, P., Girshick, R., He, K., Hariharan, B., & Belongie, S. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.106 · Cited by 30,939
Focal Loss for Dense Object Detection
Lin, T.-Y., Goyal, P., Girshick, R., He, K., & Dollar, P. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.324 · Cited by 30,581
MobileNetV2: Inverted Residuals and Linear Bottlenecks
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., & Chen, L.-C. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00474 · Cited by 28,755
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., & Adam, H. (2017) · arXiv · doi:10.48550/arxiv.1704.04861 · Cited by 19,015
End-to-End Object Detection with Transformers
Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., & Zagoruyko, S. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58452-8_13 · Cited by 18,285
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Tan, M., & Le, Q. V. (2019) · arXiv · doi:10.48550/arxiv.1905.11946 · Cited by 17,072
Decoupled Weight Decay Regularization
Loshchilov, I., & Hutter, F. (2017) · arXiv · doi:10.48550/arxiv.1711.05101 · Cited by 12,522
Squeeze-and-Excitation Networks
Hu, J., Shen, L., Albanie, S., Sun, G., & Wu, E. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2913372 · Cited by 12,157
ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
Zhang, X., Zhou, X., Lin, M., & Sun, J. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00716 · Cited by 9,982
EfficientDet: Scalable and Efficient Object Detection
Tan, M., Pang, R., & Le, Q. V. (2019) · arXiv · Cited by 9,841
Howard, A., Sandler, M., Chen, B., Wang, W., Chen, L.-C., Tan, M., Chu, G., Vasudevan, V., Zhu, Y., Pang, R., et al. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00140 · Cited by 9,708
ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design
Ma, N., Zhang, X., Zheng, H.-T., & Sun, J. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01264-9_8 · Cited by 7,356
mixup: Beyond Empirical Risk Minimization
Zhang, H., Cisse, M., Dauphin, Y. N., & Lopez-Paz, D. (2017) · arXiv · doi:10.48550/arxiv.1710.09412 · Cited by 5,831
Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions
Wang, W., Xie, E., Li, X., Fan, D.-P., Song, K., Liang, D., Lu, T., Luo, P., & Shao, L. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00061 · Cited by 5,259
GhostNet: More Features From Cheap Operations
Han, K., Wang, Y., Tian, Q., Guo, J., Xu, C., & Xu, C. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00165 · Cited by 5,222
AutoAugment: Learning Augmentation Strategies From Data
Cubuk, E. D., Zoph, B., Mané, D., Vasudevan, V., & Le, Q. V. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00020 · Cited by 4,445
Random Erasing Data Augmentation
Zhong, Z., Zheng, L., Kang, G., Li, S., & Yang, Y. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i07.7000 · Cited by 3,617
CvT: Introducing Convolutions to Vision Transformers
Wu, H., Xiao, B., Codella, N., Liu, M., Dai, X., Yuan, L., & Zhang, L. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00009 · Cited by 2,795
Self-citationTokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet
Yuan, L., Chen, Y., Wang, T., Yu, W., Shi, Y., Jiang, Z., Tay, F. E. H., Feng, J., & Yan, S. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00060 · Cited by 2,415
Self-citationCrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classification
Chen, C.-F. R., Fan, Q., & Panda, R. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00041 · Cited by 2,350
MLP-Mixer: An all-MLP Architecture for Vision
Tolstikhin, I., Houlsby, N., Kolesnikov, A., Beyer, L., Zhai, X., Unterthiner, T., Yung, J., Steiner, A., Keysers, D., Uszkoreit, J., et al. (2021) · arXiv · doi:10.48550/arxiv.2105.01601 · Cited by 2,087
Dynamic Convolution: Attention Over Convolution Kernels
Chen, Y., Dai, X., Liu, M., Chen, D., Yuan, L., & Liu, Z. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.01104 · Cited by 1,633
Self-citationCSWin Transformer: A General Vision Transformer Backbone with Cross-Shaped Windows
Dong, X., Bao, J., Chen, D., Zhang, W., Yu, N., Yuan, L., Chen, D., & Guo, B. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.01181 · Cited by 1,533
Self-citationBottleneck Transformers for Visual Recognition
Srinivas, A., Lin, T.-Y., Parmar, N., Shlens, J., Abbeel, P., & Vaswani, A. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.01625 · Cited by 1,385
Going deeper with Image Transformers
Touvron, H., Cord, M., Sablayrolles, A., Synnaeve, G., & Jegou, H. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00010 · Cited by 1,259
LeViT: a Vision Transformer in ConvNet’s Clothing for Faster Inference
Graham, B., El-Nouby, A., Touvron, H., Stock, P., Joulin, A., Jegou, H., & Douze, M. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01204 · Cited by 935
Conformer: Local Features Coupling Global Representations for Visual Recognition
Peng, Z., Huang, W. F., Gu, S., Xie, L., Wang, Y., Jiao, J., & Ye, Q. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00042 · Cited by 922
Mixup: Beyond empirical risk minimizationPreprint
Zhang, H. (2024) · arXiv (Cornell University) · doi:10.57702/dcy1c3gw · Cited by 864
ConViT: improving vision transformers with soft convolutional inductive biases
d’Ascoli, S., Touvron, H., Leavitt, M. L., Morcos, A. S., Biroli, G., & Sagun, L. (2022) · Journal of Statistical Mechanics: Theory and Experiment · doi:10.1088/1742-5468/ac9830 · Cited by 782
Early Convolutions Help Transformers See BetterPreprint
Xiao, T., Singh, M., Mintun, E., Darrell, T., Dollár, P., & Girshick, R. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.14881 · Cited by 596
CondConv: Conditionally Parameterized Convolutions for Efficient InferencePreprint
Yang, B., Bender, G., Le, Q. V., & Ngiam, J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.04971 · Cited by 582
Scaling Local Self-Attention for Parameter Efficient Visual Backbones
Vaswani, A., Ramachandran, P., Srinivas, A., Parmar, N., Hechtman, B. A., & Shlens, J. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.01270 · Cited by 496
Co-Scale Conv-Attentional Image Transformers
Xu, W., Xu, Y., Chang, T. A., & Tu, Z. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00983 · Cited by 455
(2021) · International Conference on Learning Representations · Cited by 449
MixConv: Mixed Depthwise Convolutional KernelsPreprint
Tan, M., & Le, Q. V. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.09595 · Cited by 446
DeepViT: Towards Deeper Vision Transformer
Zhou, D., Kang, B., Jin, X., Yang, L., Lian, X., Jiang, Z., Hou, Q., & Feng, J. (2021) · arXiv · doi:10.48550/arxiv.2103.11886 · Cited by 398
Rethinking Bottleneck Structure for Efficient Mobile Network Design
Zhou, D., Hou, Q., Chen, Y., Feng, J., & Yan, S. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58580-8_40 · Cited by 292
Self-citationAdderNet: Do We Really Need Multiplications in Deep Learning?
Chen, H., Wang, Y., Xu, C., Shi, B., Xu, C., Tian, Q., & Xu, C. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00154 · Cited by 199
Chen, Y., Dai, X., Liu, M., Chen, D., Yuan, L., & Liu, Z. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58529-7_21 · Cited by 190
Self-citationHow to Represent Part-Whole Hierarchies in a Neural Network
Hinton, G. E. (2022) · Neural Computation · doi:10.1162/neco_a_01557 · Cited by 173
MicroNet: Improving Image Recognition with Extremely Low FLOPs
Li, Y., Chen, Y., Dai, X., Chen, D., Liu, M., Yuan, L., Liu, Z., Zhang, L., & Vasconcelos, N. M. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00052 · Cited by 129
Self-citationWeightNet: Revisiting the Design Space of Weight Networks
Ma, N., Zhang, X., Huang, J., & Sun, J. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58555-6_46 · Cited by 108
LeViT: a Vision Transformer in ConvNet's Clothing for Faster InferencePreprint
Graham, B. V., El-Nouby, A., Touvron, H., Stock, P., Joulin, A., Jeǵou, H., & Douze, M. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.01136 · Cited by 88
ConTNet: Why not use convolution and transformer at the same time?Preprint
Yan, H., Li, Z., Li, W., Wang, C., Wu, M., & Zhang, C. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.13497 · Cited by 85
Model Rubik's Cube: Twisting Resolution, Depth and Width for TinyNetsPreprint
Han, K., Wang, Y., Zhang, Q., Zhang, W., Xu, C., & Zhang, T. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.14819 · Cited by 64
Refiner: Refining Self-attention for Vision TransformersPreprint
Zhou, D., Shi, Y., Kang, B., Yu, W., Jiang, Z., Li, Y., Jin, X., Hou, Q., & Feng, J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.03714 · Cited by 64
Butterfly Transform: An Efficient FFT Based Neural Architecture Design
Alizadeh, K., Prabhu, A., Farhadi, A., & Rastegari, M. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.01204 · Cited by 47
Flagged first, then by citations
2 of the 55 references couldn’t be matched to a known paper.
Research works printed but not linked: 3
- 10.1109/CVPR42600.2020.01079 · DOI · Paper page
- touvron, 2020, Training data-efficient image transformers and distillation through at-tention, ArXiv Preprint
- hu, 2018, Squeeze-and-excitation net-works, The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Cited by
Sorted by citations
Run, Don't Walk: Chasing Higher FLOPS for Faster Neural Networks
Chen, J., Kao, S.-H., He, H., Zhuo, W., Song, W., Lee, C.-H., & Chan, S.-H. G. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.01157 · Cited by 2,647
A review of convolutional neural networks in computer vision
Zhao, X., Wang, L., Zhang, Y., Han, X., Deveci, M., & Parmar, M. D. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10721-6 · Cited by 1,477
Transfer learning for medical image classification: a literature review
Kim, H. E., Cosa‐Linan, A., Santhanam, N., Jannesari, M., Maros, M. E., & Ganslandt, T. (2022) · BMC Medical Imaging · doi:10.1186/s12880-022-00793-7 · Cited by 1,117
EfficientViT: Memory Efficient Vision Transformer with Cascaded Group Attention
Liu, X., Peng, H., Zheng, N., Yang, Y., Hu, H., & Yuan, Y. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.01386 · Cited by 956
SCConv: Spatial and Channel Reconstruction Convolution for Feature Redundancy
Li, J., Wen, Y., & He, L. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.00596 · Cited by 823
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
About these checks
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.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
