Mobile-Former: Bridging MobileNet and Transformer

Deep Residual Learning for Image Recognition

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Attention Is All You Need

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ImageNet: A large-scale hierarchical image database

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Microsoft COCO: Common Objects in Context

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Squeeze-and-Excitation Networks

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Swin Transformer: Hierarchical Vision Transformer using Shifted Windows

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Feature Pyramid Networks for Object Detection

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Focal Loss for Dense Object Detection

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MobileNetV2: Inverted Residuals and Linear Bottlenecks

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MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

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End-to-End Object Detection with Transformers

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EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

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Decoupled Weight Decay Regularization

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Squeeze-and-Excitation Networks

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ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices

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EfficientDet: Scalable and Efficient Object Detection

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Searching for MobileNetV3

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ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design

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mixup: Beyond Empirical Risk Minimization

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Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions

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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 · see citing works

AutoAugment: Learning Augmentation Strategies From Data

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Random Erasing Data Augmentation

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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 2,649 · see citing works

Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet

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CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classification

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MLP-Mixer: An all-MLP Architecture for Vision

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Dynamic Convolution: Attention Over Convolution Kernels

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CSWin 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 1,458 · see citing works

Bottleneck 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 · see citing works

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 · see citing works

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 · see citing works

Mixup: Beyond empirical risk minimizationPreprint

Zhang, H. (2024) · arXiv (Cornell University) · doi:10.57702/dcy1c3gw · Cited by 923 · see citing works

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 900 · see citing works

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 · see citing works

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 573 · see citing works

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 565 · see citing works

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 483 · see citing works

Untitled work

(2021) · International Conference on Learning Representations · Cited by 449 · see citing works

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 425 · see citing works

MixConv: Mixed Depthwise Convolutional KernelsPreprint

Tan, M., & Le, Q. V. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.09595 · Cited by 421 · see citing works

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 · see citing works

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 · see citing works

AdderNet: 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 · see citing works

Dynamic ReLU

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 189 · see citing works

How to Represent Part-Whole Hierarchies in a Neural Network

Hinton, G. E. (2022) · Neural Computation · doi:10.1162/neco_a_01557 · Cited by 173 · see citing works

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 122 · see citing works

WeightNet: 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 105 · see citing works

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 · see citing works

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 79 · see citing works

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 63 · see citing works

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 53 · see citing works

Butterfly Transform: An Efficient FFT Based Neural Architecture Design

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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 2,556 · see citing works

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 1,326 · see citing works

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 1,036 · see citing works

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 · see citing works

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 · see citing works

MobileNetV4: Universal Models for the Mobile Ecosystem

Qin, D., Leichner, C., Delakis, M., Fornoni, M., Luo, S., Yang, F., Wang, W., Banbury, C., Ye, C., Akin, B., et al. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73661-2_5 · Cited by 746 · see citing works

Rep ViT: Revisiting Mobile CNN From ViT Perspective

Wang, A., Chen, H., Lin, Z., Han, J., & Ding, G. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52733.2024.01506 · Cited by 752 · see citing works

Rewrite the Stars

Ma, X., Dai, X., Bai, Y., Wang, Y., & Fu, Y. (2024) · 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/cvpr52733.2024.00544 · Cited by 663 · see citing works

UniFormer: Unifying Convolution and Self-Attention for Visual Recognition

Li, K., Wang, Y., Zhang, J., Gao, P., Song, G., Liu, Y., Li, H., & Qiao, Y. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3282631 · Cited by 636 · see citing works

MedViT: A robust vision transformer for generalized medical image classification

Manzari, O. N., Ahmadabadi, H., Kashiani, H., Shokouhi, S. B., & Ayatollahi, A. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.106791 · Cited by 537 · see citing works

TinyViT: Fast Pretraining Distillation for Small Vision Transformers

Wu, K., Zhang, J., Peng, H., Liu, M., Xiao, B., Fu, J., & Yuan, L. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19803-8_5 · Cited by 480 · see citing works

A survey of the vision transformers and their CNN-transformer based variants

Khan, A., Rauf, Z., Sohail, A., Khan, A. R., Asif, H., Asif, A., & Farooq, U. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10595-0 · Cited by 496 · see citing works

MobileOne: An Improved One millisecond Mobile Backbone

Vasu, P. K. A., Gabriel, J., Zhu, J. X., Tuzel, O., & Ranjan, A. (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/cvpr52729.2023.00764 · Cited by 450 · see citing works

Edgenext: Efficiently amalgamated CNN-transformer architecture for mobile vision applications

Maaz, M., Shaker, A., Cholakkal, H., Khan, S., Zamir, S. W., Anwer, R. M., & Shahbaz Khan, F. (2023) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-25082-8_1 · Cited by 407 · see citing works

DilateFormer: Multi-Scale Dilated Transformer for Visual Recognition

Jiao, J., Tang, Y.-M., Lin, K.-Y., Gao, Y., Ma, A. J., Wang, Y., & Zheng, W. (2023) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2023.3243616 · Cited by 412 · see citing works

Transforming medical imaging with Transformers? A comparative review of key properties, current progresses, and future perspectives

Li, J., Chen, J., Tang, Y., Wang, C., Landman, B. A., & Zhou, S. K. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.102762 · Cited by 426 · see citing works

A review of convolutional neural network architectures and their optimizations

Cong, S., & Zhou, Y. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-022-10213-5 · Cited by 397 · see citing works

TopFormer: Token Pyramid Transformer for Mobile Semantic Segmentation

Zhang, W., Huang, Z., Luo, G., Chen, T., Wang, X., Liu, W., Yu, G., & Shen, C. (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/cvpr52688.2022.01177 · Cited by 374 · see citing works

Rethinking Vision Transformers for MobileNet Size and Speed

Li, Y., Ju, H., Wen, Y., Evangelidis, G., Salahi, K., Wang, Y., Tulyakov, S., & Ren, J. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01549 · Cited by 369 · see citing works

FLatten Transformer: Vision Transformer using Focused Linear Attention

Han, D., Pan, X., Han, Y., Song, S., & Huang, G. M. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00548 · Cited by 359 · see citing works

Rethinking Mobile Block for Efficient Attention-based Models

Zhang, J., Li, X., Li, J., Liu, L., Xue, Z., Zhang, B., Jiang, Z., Huang, T., Wang, Y., & Wang, C. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00134 · Cited by 346 · see citing works

EfficientViT: Lightweight Multi-Scale Attention for High-Resolution Dense Prediction

Cai, H., Li, J., Hu, M., Gan, C., & Han, S. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01587 · Cited by 356 · see citing works

Spatially-Adaptive Feature Modulation for Efficient Image Super-Resolution

Sun, L., Dong, J., Tang, J., & Pan, J. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01213 · Cited by 308 · see citing works

SwiftFormer: Efficient Additive Attention for Transformer-based Real-time Mobile Vision Applications

Shaker, A., Maaz, M., Rasheed, H., Khan, S., Yang, M., & Khan, F. S. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01598 · Cited by 299 · see citing works

ViTAEv2: Vision Transformer Advanced by Exploring Inductive Bias for Image Recognition and Beyond

Zhang, Q., Xu, Y., Zhang, J., & Tao, D. (2023) · International Journal of Computer Vision · doi:10.1007/s11263-022-01739-w · Cited by 465 · see citing works

Lightweight Deep Learning for Resource-Constrained Environments: A Survey

Liu, H.-I., Galindo, M., Xie, H., Wong, L.-K., Shuai, H.-H., Li, Y., & Cheng, W.-H. (2024) · ACM Computing Surveys · doi:10.1145/3657282 · Cited by 296 · see citing works

A Transformer-Based Feature Segmentation and Region Alignment Method for UAV-View Geo-Localization

Dai, M., Hu, J., Zhuang, J., & Zheng, E. (2021) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2021.3135013 · Cited by 254 · see citing works

Conv2Former: A Simple Transformer-Style ConvNet for Visual Recognition

Hou, Q., Lu, C.-Z., Cheng, M., & Feng, J. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3401450 · Cited by 256 · see citing works

BEVT: BERT Pretraining of Video Transformers

Wang, R., Chen, D., Wu, Z., Chen, Y., Dai, X., Liu, M., Jiang, Y.-G., Zhou, L., & Yuan, L. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01432 · Cited by 234 · see citing works

Emotion recognition in EEG signals using deep learning methods: A review

Jafari, M., Shoeibi, A., Khodatars, M., Bagherzadeh, S., Shalbaf, A., García, D. L., Gorriz, J. M., & Acharya, U. R. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107450 · Cited by 240 · see citing works

Omni Aggregation Networks for Lightweight Image Super-Resolution

Wang, H., Chen, X., Ni, B., Liu, Y., & Liu, J. (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/cvpr52729.2023.02143 · Cited by 227 · see citing works

Scale-Aware Modulation Meet Transformer

Lin, W., Wu, Z., Chen, J., Huang, J., & Jin, L. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00553 · Cited by 229 · see citing works

SHViT: Single-Head Vision Transformer with Memory Efficient Macro Design

Yun, S., & Ro, Y. (2024) · 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/cvpr52733.2024.00550 · Cited by 208 · see citing works

Protecting Celebrities from DeepFake with Identity Consistency Transformer

Dong, X., Bao, J., Chen, D., Zhang, T., Zhang, W., Yu, N., Chen, D., Wen, F., & Guo, B. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.00925 · Cited by 181 · see citing works

Review of Lightweight Deep Convolutional Neural Networks

Chen, F., Li, S., Han, J., Ren, F., & Yang, Z. (2023) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-023-10032-z · Cited by 172 · see citing works

Lite Vision Transformer with Enhanced Self-Attention

Yang, C., Wang, Y., Zhang, J., Zhang, H., Wei, Z., Lin, Z., & Yuille, A. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01169 · Cited by 164 · see citing works

Convolution Transformer Mixer for Hyperspectral Image Classification

Zhang, J., Meng, Z., Zhao, F., Liu, H., & Chang, Z. (2022) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2022.3208935 · Cited by 157 · see citing works

MobileRaT: A Lightweight Radio Transformer Method for Automatic Modulation Classification in Drone Communication Systems

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A Survey of Techniques for Optimizing Transformer Inference

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EfficientFormer: Vision Transformers at MobileNet Speed

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Parameter-Efficient Vision Transformer with Linear Attention

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