Fetching the paper…
Reading the bibliography…
We present Mobile-Former, a parallel design of MobileNet and transformer with a two-way bridge in between.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 2014
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Mobilenets: Efficient convolutional neural networks for mobile vision applications
Andrew G Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam · 2017
Earlier work this paper cites.
Feature pyramid networks for object detection
T. Lin, P. Dollar, R. Girshick, K. He, B. Hariharan, and S. Belongie · 2017
Earlier work this paper cites.
Focal loss for dense object detection
Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollar · 2017
Earlier work this paper cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz Kaiser, and Illia Polosukhin · 2017
Earlier work this paper cites.
Squeeze-and-excitation networks
Jie Hu, Li Shen, and Gang Sun · 2018
Earlier work this paper cites.
Shufflenet v2: Practical guidelines for efficient cnn architecture design
Ningning Ma, Xiangyu Zhang, Hai-Tao Zheng, and Jian Sun · 2018
Earlier work this paper cites.
Mobilenetv2: Inverted residuals and linear bottlenecks
Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, and Liang-Chieh Chen · 2018
Earlier work this paper cites.
mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cisse, Yann N. Dauphin, and David Lopez-Paz · 2018
Earlier work this paper cites.
Shufflenet: An extremely efficient convolutional neural network for mobile devices
Xiangyu Zhang, Xinyu Zhou, Mengxiao Lin, and Jian Sun · 2018
Earlier work this paper cites.
Autoaugment: Learning augmentation strategies from data
Ekin D. Cubuk, Barret Zoph, Dandelion Mane, Vijay Vasudevan, and Quoc V. Le · 2019
Earlier work this paper cites.
Searching for mobilenetv3
Andrew Howard, Mark Sandler, Grace Chu, Liang-Chieh Chen, Bo Chen, Mingxing Tan, Weijun Wang, Yukun Zhu, Ruoming Pang, Vijay Vasudevan, Quoc V. Le, and Hartwig Adam · 2019
Earlier work this paper cites.
Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2019
Earlier work this paper cites.
Efficientnet: Rethinking model scaling for convolutional neural networks
Mingxing Tan and Quoc Le · 2019
Earlier work this paper cites.
Mixconv: Mixed depthwise convolutional kernels
Mingxing Tan and Quoc V. Le · 2019
Earlier work this paper cites.
Pytorch image models
Ross Wightman · 2019
Cited alongside, same era.
Condconv: Conditionally parameterized convolutions for efficient inference
Brandon Yang, Gabriel Bender, Quoc V. Le, and Jiquan Ngiam · 2019
Cited alongside, same era.
End-to-end object detection with transformers
Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko · 2020
Cited alongside, same era.
Addernet: Do we really need multiplications in deep learning?
Hanting Chen, Yunhe Wang, Chunjing Xu, Boxin Shi, Chao Xu, Qi Tian, and Chang Xu · 2020
Cited alongside, same era.
Dynamic convolution: Attention over convolution kernels
Yinpeng Chen, Xiyang Dai, Mengchen Liu, Dongdong Chen, Lu Yuan, and Zicheng Liu · 2020
Cited alongside, same era.
Dynamic relu
Yinpeng Chen, Xiyang Dai, Mengchen Liu, Dongdong Chen, Lu Yuan, and Zicheng Liu · 2020
Cited alongside, same era.
Levit: a vision transformer in convnet’s clothing for faster inference
Benjamin Graham, Alaaeldin El-Nouby, Hugo Touvron, Pierre Stock, Armand Joulin, Hervé Jégou, and Matthijs Douze · 2021
Closest in time.
How to represent part-whole hierarchies in a neural network
Geoffrey E. Hinton · 2021
Closest in time.
Micronet: Improving image recognition with extremely low flops
Yunsheng Li, Yinpeng Chen, Xiyang Dai, Dongdong Chen, Mengchen Liu, Lu Yuan, Zicheng Liu, Lei Zhang, and Nuno Vasconcelos · 2021
Closest in time.
Swin transformer: Hierarchical vision transformer using shifted windows
Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo · 2021
Closest in time.
Bottleneck transformers for visual recognition
Aravind Srinivas, Tsung-Yi Lin, Niki Parmar, Jonathon Shlens, Pieter Abbeel, and Ashish Vaswani · 2021
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Ghostnet: More features from cheap operations
Kai Han, Yunhe Wang, Qi Tian, Jianyuan Guo, Chunjing Xu, and Chang Xu · 2020
Cited alongside, same era.
Model rubiks cube: Twisting resolution, depth and width for tinynets
Kai Han, Yunhe Wang, Qiulin Zhang, Wei Zhang, Chunjing XU, and Tong Zhang · 2020
Cited alongside, same era.
Weightnet: Revisiting the design space of weight networks
Ningning Ma, X. Zhang, J. Huang, and J. Sun · 2020
Cited alongside, same era.
Efficientdet: Scalable and efficient object detection
Mingxing Tan, Ruoming Pang, and Quoc V. Le · 2020
Cited alongside, same era.
Training data-efficient image transformers and distillation through attention
Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, and Hervé Jégou · 2020
Cited alongside, same era.
Butterfly transform: An efficient fft based neural architecture design
Keivan Alizadeh vahid, Anish Prabhu, Ali Farhadi, and Mohammad Rastegari · 2020
Cited alongside, same era.
Mlp-mixer: An all-mlp architecture for vision, 2021
Ilya Tolstikhin, Neil Houlsby, Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Thomas Unterthiner, Jessica Yung, Andreas Steiner, Daniel Keysers, Jakob Uszkoreit, Mario Lucic, and Alexey Dosovitskiy · 2021
Closest in time.
Going deeper with image transformers, 2021
Hugo Touvron, Matthieu Cord, Alexandre Sablayrolles, Gabriel Synnaeve, and Hervé Jégou · 2021
Closest in time.
Scaling local self-attention for parameter efficient visual backbones
Ashish Vaswani, Prajit Ramachandran, Aravind Srinivas, Niki Parmar, Blake Hechtman, and Jonathon Shlens · 2021
Closest in time.
Pyramid vision transformer: A versatile backbone for dense prediction without convolutions, 2021
Wenhai Wang, Enze Xie, Xiang Li, Deng-Ping Fan, Kaitao Song, Ding Liang, Tong Lu, Ping Luo, and Ling Shao · 2021
Closest in time.
Cvt: Introducing convolutions to vision transformers, 2021
Haiping Wu, Bin Xiao, Noel Codella, Mengchen Liu, Xiyang Dai, Lu Yuan, and Lei Zhang · 2021
Closest in time.
Early convolutions help transformers see better
Tete Xiao, Mannat Singh, Eric Mintun, Trevor Darrell, Piotr Dollár, and Ross B. Girshick · 2021
Closest in time.
Co-scale conv-attentional image transformers, 2021
Weijian Xu, Yifan Xu, Tyler Chang, and Zhuowen Tu · 2021
Closest in time.
Contnet: Why not use convolution and transformer at the same time?
Haotian Yan, Zhe Li, Weijian Li, Changhu Wang, Ming Wu, and Chuang Zhang · 2021
Closest in time.
Tokens-to-token vit: Training vision transformers from scratch on imagenet
Li Yuan, Yunpeng Chen, Tao Wang, Weihao Yu, Yujun Shi, Francis EH Tay, Jiashi Feng, and Shuicheng Yan · 2021
Closest in time.
Deepvit: Towards deeper vision transformer, 2021
Daquan Zhou, Bingyi Kang, Xiaojie Jin, Linjie Yang, Xiaochen Lian, Zihang Jiang, Qibin Hou, and Jiashi Feng · 2021
Closest in time.
Refiner: Refining self-attention for vision transformers, 2021
Daquan Zhou, Yujun Shi, Bingyi Kang, Weihao Yu, Zihang Jiang, Yuan Li, Xiaojie Jin, Qibin Hou, and Jiashi Feng · 2021
Closest in time.