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It is well known that contextual and multi-scale representations are important for accurate visual recognition.
Representation of local geometry in the visual system
J. J. Koenderink and A. J. van Doorn · 1987
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Original approach for the localisation of objects in images
R. Vaillant, C. Monrocq, and Y. LeCun · 1994
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Long short-term memory
S. Hochreiter and J. Schmidhuber · 1997
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Bidirectional recurrent neural networks
M. Schuster and K. K. Paliwal · 1997
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Modeling the shape of the scene: a holistic representation of the spatial envelope
A. Oliva and A. Torralba · 2001
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Contextual priming for object detection
A. Torralba · 2003
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An empirical study of context in object detection
S. Divvala, D. Hoiem, J. Hays, A. Efros, and M. Hebert · 2009
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Offline handwriting recognition with multidimensional recurrent neural networks
A. Graves and J. Schmidhuber · 2009
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The PASCAL Visual Object Classes (VOC) Challenge
M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman · 2010
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Xavier glorot and yoshua bengio
U. the difficulty of training deep feedforward neural networks · 2010
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Semantic contours from inverse detectors
B. Hariharan, P. Arbelaez, L. Bourdev, S. Maji, and J. Malik · 2011
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Exploring tiny images: The roles of appearance and contextual information for machine and human object recognition
D. Parikh, C. L. Zitnick, and T. Chen · 2011
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Diagnosing error in object detectors
D. Hoiem, Y. Chodpathumwan, and Q. Dai · 2012
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ImageNet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. Hinton · 2012
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Pedestrian detection with unsupervised multi-stage feature learning
P. Sermanet, K. Kavukcuoglu, S. Chintala, and Y. LeCun · 2013
Cited alongside, same era.
Selective search for object recognition
J. Uijlings, K. van de Sande, T. Gevers, and A. Smeulders · 2013
Cited alongside, same era.
Analyzing the performance of multilayer neural networks for object recognition
P. Agrawal, R. Girshick, and J. Malik · 2014
Cited alongside, same era.
Return of the devil in the details: Delving deep into convolutional nets
K. Chatfield, K. Simonyan, A. Vedaldi, and A. Zisserman · 2014
Cited alongside, same era.
On the properties of neural machine translation: Encoder-decoder approaches
K. Cho, B. van Merrienboer, D. Bahdanau, and Y. Bengio · 2014
Cited alongside, same era.
Rich feature hierarchies for accurate object detection and semantic segmentation
Hypercolumns for object segmentation and fine-grained localization
B. Hariharan, P. Arbeláez, R. Girshick, and J. Malik · 2015
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What makes for effective detection proposals?
J. H. Hosang, R. Benenson, P. Dollár, and B. Schiele · 2015
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Visualizing and understanding recurrent networks
A. Karpathy, J. Johnson, and F.-F. Li · 2015
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A simple way to initialize recurrent networks of rectified linear units
Q. V. Le, N. Jaitly, and G. E. Hinton · 2015
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ParseNet: Looking wider to see better
W. Liu, A. Rabinovich, and A. C. Berg · 2015
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R. Girshick, J. Donahue, T. Darrell, and J. Malik · 2014
Cited alongside, same era.
Spatial pyramid pooling in deep convolutional networks for visual recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2014
Cited alongside, same era.
Caffe: Convolutional architecture for fast feature embedding
Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell · 2014
Cited alongside, same era.
Microsoft COCO: common objects in context
T. Lin, M. Maire, S. Belongie, L. Bourdev, R. Girshick, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick · 2014
Cited alongside, same era.
ImageNet Large Scale Visual Recognition Challenge
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei · 2014
Cited alongside, same era.
OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks
P. Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus, and Y. LeCun · 2014
Cited alongside, same era.
Scene labeling with lstm recurrent neural networks
W. Byeon, T. M. Breuel, F. Raue, and M. Liwicki · 2015
Cited alongside, same era.
Fully convolutional networks for semantic segmentation
J. Long, E. Shelhamer, and T. Darrell · 2015
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Learning to segment object candidates
P. O. Pinheiro, R. Collobert, and P. Dollár · 2015
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Multiscale combinatorial grouping for image segmentation and object proposal generation
J. Pont-Tuset, P. Arbeláez, J. Barron, F. Marques, and J. Malik · 2015
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You only look once: Unified, real-time object detection
J. Redmon, S. Divvala, R. Girshick, and A. Farhadi · 2015
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Faster R-CNN: Towards real-time object detection with region proposal networks
S. Ren, K. He, R. Girshick, and J. Sun · 2015
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2015
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ReNet: A recurrent neural network based alternative to convolutional networks
F. Visin, K. Kastner, K. Cho, M. Matteucci, A. Courville, and Y. Bengio · 2015
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Edge boxes: Locating object proposals from edges
C. L. Zitnick and P. Dollár · 2015
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