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Human vision is able to capture the part-whole hierarchical information from the entire scene.
Mmdetection: Open mmlab detection toolbox and benchmark
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Rich feature hierarchies for accurate object detection and semantic segmentation
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Adam: A method for stochastic optimization
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Microsoft coco: Common objects in context
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Fast R-CNN
Ross Girshick · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
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Fully convolutional networks for semantic segmentation
Jonathan Long, Evan Shelhamer, and Trevor Darrell · 2015
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Faster r-cnn: Towards real-time object detection with region proposal networks
Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun · 2015
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2015
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End-to-end memory networks
Sainbayar Sukhbaatar, Arthur Szlam, Jason Weston, and Rob Fergus · 2015
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Going deeper with convolutions
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Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Gaussian error linear units (gelus)
Dan Hendrycks and Kevin Gimpel · 2016
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Deep networks with stochastic depth
Gao Huang, Yu Sun, Zhuang Liu, Daniel Sedra, and Kilian Q Weinberger · 2016
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Dynamic filter networks
Xu Jia, Bert De Brabandere, Tinne Tuytelaars, and Luc V Gool · 2016
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Ssd: Single shot multibox detector
Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott E. Reed, Cheng-Yang Fu, and Alexander C. Berg · 2016
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Multi-scale context aggregation by dilated convolutions
Fisher Yu and Vladlen Koltun · 2016
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Segnet: A deep convolutional encoder-decoder architecture for image segmentation
Vijay Badrinarayanan, Alex Kendall, and Roberto Cipolla · 2017
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Mask r-cnn
Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick · 2017
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Densely connected convolutional networks
Gao Huang, Zhuang Liu, Kilian Q Weinberger, and Laurens van der Maaten · 2017
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Dynamic routing between capsules
Sara Sabour, Nicholas Frosst, and Geoffrey E Hinton · 2017
On the relationship between self-attention and convolutional layers
Jean-Baptiste Cordonnier, Andreas Loukas, and Martin Jaggi · 2020
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Object-centric learning with slot attention
Francesco Locatello, Dirk Weissenborn, Thomas Unterthiner, Aravindh Mahendran, Georg Heigold, Jakob Uszkoreit, Alexey Dosovitskiy, and Thomas Kipf · 2020
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Designing network design spaces
Ilija Radosavovic, Raj Prateek Kosaraju, Ross Girshick, Kaiming He, and Piotr Dollár · 2020
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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 · 2020
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Capsules with inverted dot-product attention routing
Yao-Hung Hubert Tsai, Nitish Srivastava, Hanlin Goh, and Ruslan Salakhutdinov · 2020
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
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Pyramid scene parsing network
Hengshuang Zhao, Jianping Shi, Xiaojuan Qi, Xiaogang Wang, and Jiaya Jia · 2017
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Cascade r-cnn: Delving into high quality object detection
Zhaowei Cai and Nuno Vasconcelos · 2018
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a 2 a^{2} -nets: Double attention networks
Yunpeng Chen, Yannis Kalantidis, Jianshu Li, Shuicheng Yan, and Jiashi Feng · 2018
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Matrix capsules with em routing
Geoffrey E Hinton, Sara Sabour, and Nicholas Frosst · 2018
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Non-local neural networks
Xiaolong Wang, Ross Girshick, Abhinav Gupta, and Kaiming He · 2018
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Visual transformers: Token-based image representation and processing for computer vision
Bichen Wu, Chenfeng Xu, Xiaoliang Dai, Alvin Wan, Peizhao Zhang, Masayoshi Tomizuka, Kurt Keutzer, and Peter Vajda · 2020
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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 · 2020
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Exploring self-attention for image recognition
Hengshuang Zhao, Jiaya Jia, and Vladlen Koltun · 2020
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Lambda networks: Modeling long-range interactions without attention
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High-performance large-scale image recognition without normalization
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Multiscale vision transformers
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Rethinking spatial dimensions of vision transformers
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How to represent part-whole hierarchies in a neural network
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Swin transformer: Hierarchical vision transformer using shifted windows
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