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The recent amalgamation of transformer and convolutional designs has led to steady improvements in accuracy and efficiency of the models.
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
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Imagenet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Mask r-cnn
Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick · 2017
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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
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Embodied hands: Modeling and capturing hands and bodies together
Javier Romero, Dimitrios Tzionas, and Michael J. Black · 2017
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Aggregated residual transformations for deep neural networks
Saining Xie, Ross Girshick, Piotr Dollár, Zhuowen Tu, and Kaiming He · 2017
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Scene parsing through ade20k dataset
Bolei Zhou, Hang Zhao, Xavier Puig, Sanja Fidler, Adela Barriuso, and Antonio Torralba · 2017
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Shufflenet v2: Practical guidelines for efficient cnn architecture design
Ningning Ma, Xiangyu Zhang, Hai-Tao Zheng, and Jian Sun · 2018
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Mobilenetv2: Inverted residuals and linear bottlenecks
Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, and Liang-Chieh Chen · 2018
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Shufflenet: An extremely efficient convolutional neural network for mobile devices
Xiangyu Zhang, Xinyu Zhou, Mengxiao Lin, and Jian Sun · 2018
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Acnet: Strengthening the kernel skeletons for powerful cnn via asymmetric convolution blocks
Xiaohan Ding, Yuchen Guo, Guiguang Ding, and Jungong Han · 2019
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Light-weight core library that provides the most common and essential functionality shared in various computer vision frameworks developed in fair
fvcore · 2019
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Learning joint reconstruction of hands and manipulated objects
Yana Hasson, Gül Varol, Dimitris Tzionas, Igor Kalevatykh, Michael J. Black, Ivan Laptev, and Cordelia Schmid · 2019
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Benchmarking neural network robustness to common corruptions and perturbations
Dan Hendrycks and Thomas Dietterich · 2019
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Searching for mobilenetv3
Andrew Howard, Mark Sandler, Grace Chu, Liang-Chieh Chen, Bo Chen, Mingxing Tan, Weijun Wang, Yukun Zhu, Ruoming Pang, Vijay Vasudevan, et al · 2019
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Panoptic feature pyramid networks
Alexander Kirillov, Ross Girshick, Kaiming He, and Piotr Dollár · 2019
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Deep high-resolution representation learning for human pose estimation
Ke Sun, Bin Xiao, Dong Liu, and Jingdong Wang · 2019
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Mnasnet: Platform-aware neural architecture search for mobile
Mingxing Tan, Bo Chen, Ruoming Pang, Vijay Vasudevan, Mark Sandler, Andrew Howard, and Quoc V. Le · 2019
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Efficientnet: Rethinking model scaling for convolutional neural networks
Mingxing Tan and Quoc Le · 2019
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Learning robust global representations by penalizing local predictive power
Haohan Wang, Songwei Ge, Zachary Lipton, and Eric P Xing · 2019
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Pytorch image models
Ross Wightman · 2019
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On the continuity of rotation representations in neural networks
Yi Zhou, Connelly Barnes, Lu Jingwan, Yang Jimei, and Li Hao · 2019
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Freihand: A dataset for markerless capture of hand pose and shape from single rgb images
Christian Zimmermann, Duygu Ceylan, Jimei Yang, Bryan Russell, Max Argus, and Thomas Brox · 2019
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Pose2mesh: Graph convolutional network for 3d human pose and mesh recovery from a 2d human pose
Hongsuk Choi, Gyeongsik Moon, and Kyoung Mu Lee · 2020
Cited alongside, same era.
An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2020
Cited alongside, same era.
Mobilehand: Real-time 3d hand shape and pose estimation from color image
Lim Guan Ming, Jatesiktat Prayook, and Ang Wei Tech · 2020
Cited alongside, same era.
Expandnets: Linear over-parameterization to train compact convolutional networks
Shuxuan Guo, Jose M. Alvarez, and Mathieu Salzmann · 2020
Cited alongside, same era.
Reformer: The efficient transformer
Nikita Kitaev, Lukasz Kaiser, and Anselm Levskaya · 2020
Cited alongside, same era.
Token pooling in vision transformers
Dmitrii Marin, Jen-Hao Rick Chang, Anurag Ranjan, Anish Prabhu, Mohammad Rastegari, and Oncel Tuzel · 2021
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Efficientnetv2: Smaller models and faster training
Mingxing Tan and Quoc V. Le · 2021
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Towards accurate alignment in real-time 3d hand-mesh reconstruction
Xiao Tang, Tianyu Wang, and Chi-Wing Fu · 2021
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Training data-efficient image transformers & distillation through attention
Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, and Hervé Jégou · 2021
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Going deeper with image transformers
Hugo Touvron, Matthieu Cord, Alexandre Sablayrolles, Gabriel Synnaeve, and Hervé Jégou · 2021
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Pyramid vision transformer: A versatile backbone for dense prediction without convolutions
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Weakly-supervised mesh-convolutional hand reconstruction in the wild
Dominik Kulon, Riza Alp Guler, Iasonas Kokkinos, Michael M. Bronstein, and Stefanos Zafeiriou · 2020
Cited alongside, same era.
I2l-meshnet: Image-to-lixel prediction network for accurate 3d human pose and mesh estimation from a single rgb image
Gyeongsik Moon and Kyoung Mu Lee · 2020
Cited alongside, same era.
Designing network design spaces
Ilija Radosavovic, Raj Prateek Kosaraju, Ross Girshick, Kaiming He, and Piotr Dollár · 2020
Cited alongside, same era.
Linformer: Self-attention with linear complexity, 2020
Sinong Wang, Belinda Z. Li, Madian Khabsa, Han Fang, and Hao Ma · 2020
Cited alongside, same era.
Rethinking bottleneck structure for efficient mobile network design
Daquan Zhou, Qibin Hou, Yunpeng Chen, Jiashi Feng, and Shuicheng Yan · 2020
Cited alongside, same era.
High-performance large-scale image recognition without normalization
Andrew Brock, Soham De, Samuel L. Smith, and Karen Simonyan · 2021
Cited alongside, same era.
I2uv-handnet: Image-to-uv prediction network for accurate and high-fidelity 3d hand mesh modeling
Ping Chen, Yujin Chen, Dong Yang, Fangyin Wu, Qin Li, Qingpei Xia, and Yong Tan · 2021
Cited alongside, same era.
Wenhai Wang, Enze Xie, Xiang Li, Deng-Ping Fan, Kaitao Song, Ding Liang, Tong Lu, Ping Luo, and Ling Shao · 2021
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Resnet strikes back: An improved training procedure in timm, 2021
Ross Wightman, Hugo Touvron, and Hervé Jégou · 2021
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Cvt: Introducing convolutions to vision transformers
Haiping Wu, Bin Xiao, Noel Codella, Mengchen Liu, Xiyang Dai, Lu Yuan, and Lei Zhang · 2021
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Early convolutions help transformers see better
Tete Xiao, Mannat Singh, Eric Mintun, Trevor Darrell, Piotr Dollár, and Ross B. Girshick · 2021
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Shuangfei Zhai, Walter Talbott, Nitish Srivastava, Chen Huang, Hanlin Goh, Ruixiang Zhang, and Josh Susskind · 2021
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Hand image understanding via deep multi-task learning
Xiong Zhang, Hongsheng Huang, Jianchao Tan, Hongmin Xu, Cheng Yang, Guozhu Peng, Lei Wang, and Ji Liu · 2021
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Mobrecon: Mobile-friendly hand mesh reconstruction from monocular image
Xingyu Chen, Yufeng Liu, Dong Yajiao, Xiong Zhang, Chongyang Ma, Yanmin Xiong, Yuan Zhang, and Xiaoyan Guo · 2022
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Efficientformer: Vision transformers at mobilenet speed
Yanyu Li, Geng Yuan, Yang Wen, Eric Hu, Georgios Evangelidis, Sergey Tulyakov, Yanzhi Wang, and Jian Ren · 2022
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Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, and Saining Xie · 2022
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Towards robust vision transformer
Xiaofeng Mao, Gege Qi, Yuefeng Chen, Xiaodan Li, Ranjie Duan, Shaokai Ye, Yuan He, and Hui Xue · 2022
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Edgevits: Competing light-weight cnns on mobile devices with vision transformers
Junting Pan, Adrian Bulat, Fuwen Tan, Xiatian Zhu, Lukasz Dudziak, Hongsheng Li, Georgios Tzimiropoulos, and Brais Martinez · 2022
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Fast vision transformers with hilo attention
Zizheng Pan, Jianfei Cai, and Bohan Zhuang · 2022
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Less is more: Pay less attention in vision transformers
Zizheng Pan, Bohan Zhuang, Haoyu He, Jing Liu, and Jianfei Cai · 2022
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Asher Trockman and J Zico Kolter · 2022
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Maxvit: Multi-axis vision transformer
Zhengzhong Tu, Hossein Talebi, Han Zhang, Feng Yang, Peyman Milanfar, Alan Bovik, and Yinxiao Li · 2022
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An improved one millisecond mobile backbone
Pavan Kumar Anasosalu Vasu, James Gabriel, Jeff Zhu, Oncel Tuzel, and Anurag Ranjan · 2022
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Can cnns be more robust than transformers?
Zeyu Wang, Yutong Bai, Yuyin Zhou, and Cihang Xie · 2022
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Metaformer is actually what you need for vision
Weihao Yu, Mi Luo, Pan Zhou, Chenyang Si, Yichen Zhou, Xinchao Wang, Jiashi Feng, and Shuicheng Yan · 2022
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Resnest: Split-attention networks
Hang Zhang, Chongruo Wu, Zhongyue Zhang, Yi Zhu, Haibin Lin, Zhi Zhang, Yue Sun, Tong He, Jonas Mueller, R Manmatha, et al · 2022
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MOAT: Alternating mobile convolution and attention brings strong vision models
Chenglin Yang et al · 2023
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