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While the use of bottom-up local operators in convolutional neural networks (CNNs) matches well some of the statistics of natural images, it may also prevent such models from capturing contextual long-range feature interactions.
Contextual relations: the influence of familiarity, physical plausibility, and belongingness
Howard S Hock, Gregory P Gordon, and Robert Whitehurst · 1974
Earlier work this paper cites.
Visions: A computer system for interpreting scenes
A Hanson · 1978
Earlier work this paper cites.
Scene perception: Detecting and judging objects undergoing relational violations
Irving Biederman, Robert J Mezzanotte, and Jan C Rabinowitz · 1982
Earlier work this paper cites.
Distributed representations
Geoffrey E Hinton, James L McClelland, David E Rumelhart, et al · 1986
Earlier work this paper cites.
Context-based vision: recognizing objects using information from both 2D and 3D imagery
Thomas M Strat and Martin A Fischler · 1991
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
Earlier work this paper cites.
Contextual priming for object detection
Antonio Torralba · 2003
Earlier work this paper cites.
Context-based vision system for place and object recognition
Antonio Torralba, Kevin P Murphy, William T Freeman, Mark A Rubin, et al · 2003
Earlier work this paper cites.
Visual categorization with bags of keypoints
Gabriella Csurka, Christopher Dance, Lixin Fan, Jutta Willamowski, and Cédric Bray · 2004
Earlier work this paper cites.
Using the forest to see the trees: A graphical model relating features, objects, and scenes
Kevin P Murphy, Antonio Torralba, and William T Freeman · 2004
Earlier work this paper cites.
A critical view of context
Lior Wolf and Stanley Bileschi · 2006
Earlier work this paper cites.
Evaluating bag-of-visual-words representations in scene classification
Jun Yang, Yu-Gang Jiang, Alexander G Hauptmann, and Chong-Wah Ngo · 2007
Earlier work this paper cites.
Learning spatial context: Using stuff to find things
Geremy Heitz and Daphne Koller · 2008
Earlier work this paper cites.
An empirical study of context in object detection
Santosh K Divvala, Derek Hoiem, James H Hays, Alexei A Efros, and Martial Hebert · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
Earlier work this paper cites.
Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations
Honglak Lee, Roger Grosse, Rajesh Ranganath, and Andrew Y Ng · 2009
Earlier work this paper cites.
ImageNet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
Earlier work this paper cites.
Image classification with the fisher vector: Theory and practice
Jorge Sanchez, Florent Perronnin, Thomas Mensink, and Jakob Verbeek · 2013
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Network in network
Min Lin, Qiang Chen, and Shuicheng Yan · 2014
Cited alongside, same era.
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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The role of context for object detection and semantic segmentation in the wild
Roozbeh Mottaghi, Xianjie Chen, Xiaobai Liu, Nam-Gyu Cho, Seong-Whan Lee, Sanja Fidler, Raquel Urtasun, and Alan Yuille · 2014
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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
Cited alongside, same era.
ImageNet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, and Michael Bernstein · 2015
Multi-scale context aggregation by dilated convolutions
Fisher Yu and Vladlen Koltun · 2016
Later among the works it cites.
Wide residual networks
Sergey Zagoruyko and Nikos Komodakis · 2016
Later among the works it cites.
SCA-CNN: Spatial and channel-wise attention in convolutional networks for image captioning
Long Chen, Hanwang Zhang, Jun Xiao, Liqiang Nie, Jian Shao, Wei Liu, and Tat-Seng Chua · 2017
Later among the works it cites.
Mask R-CNN
Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick · 2017
Later among the works it 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
Later among the works it cites.
Multigrid neural architectures
Tsung-Wei Ke, Michael Maire, and X Yu Stella · 2017
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2015
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Going deeper with convolutions
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich · 2015
Cited alongside, same era.
Show, attend and tell: Neural image caption generation with visual attention
Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron Courville, Ruslan Salakhutdinov, Richard Zemel, and Yoshua Bengio · 2015
Cited alongside, same era.
Inside-outside net: Detecting objects in context with skip pooling and recurrent neural networks
Sean Bell, C Lawrence Zitnick, Kavita Bala, and Ross Girshick · 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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Identity mappings in deep residual networks
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Residual attention network for image classification
Fei Wang, Mengqing Jiang, Chen Qian, Shuo Yang, Cheng Li, Honggang Zhang, Xiaogang Wang, and Xiaoou Tang · 2017
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DeepLab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs
Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, and Alan L Yuille · 2018
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On the importance of single directions for generalization
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Self-supervised learning of geometrically stable features through probabilistic introspection
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Learning transferable architectures for scalable image recognition
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