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Visual recognition requires rich representations that span levels from low to high, scales from small to large, and resolutions from fine to coarse.
Gradient-based learning applied to document recognition
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner · 1998
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Pattern recognition and machine learning
C. M. Bishop · 2006
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Semantic object classes in video: A high-definition ground truth database
G. J. Brostow, J. Fauqueur, and R. Cipolla · 2009
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The pascal visual object classes (voc) challenge
M. Everingham, L. Van Gool, C. K. Williams, J. Winn, and A. Zisserman · 2010
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Contour detection and hierarchical image segmentation
P. Arbelaez, M. Maire, C. Fowlkes, and J. Malik · 2011
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The caltech-ucsd birds-200-2011 dataset
C. Wah, S. Branson, P. Welinder, P. Perona, and S. Belongie · 2011
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Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
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Structured forests for fast edge detection
P. Dollár and C. L. Zitnick · 2013
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Some improvements on deep convolutional neural network based image classification
A. G. Howard · 2013
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3d object representations for fine-grained categorization
J. Krause, M. Stark, J. Deng, and L. Fei-Fei · 2013
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Fine-grained visual classification of aircraft
S. Maji, E. Rahtu, J. Kannala, M. Blaschko, and A. Vedaldi · 2013
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Food-101–mining discriminative components with random forests
L. Bossard, M. Guillaumin, and L. Van Gool · 2014
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Decaf: A deep convolutional activation feature for generic visual recognition
J. Donahue, Y. Jia, O. Vinyals, J. Hoffman, N. Zhang, E. Tzeng, and T. Darrell · 2014
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Network in network
M. Lin, Q. Chen, and S. Yan · 2014
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How transferable are features in deep neural networks?
J. Yosinski, J. Clune, Y. Bengio, and H. Lipson · 2014
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Visualizing and understanding convolutional networks
M. D. Zeiler and R. Fergus · 2014
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Segnet: A deep convolutional encoder-decoder architecture for image segmentation
V. Badrinarayanan, A. Kendall, and R. Cipolla · 2015
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DeepEdge: A multi-scale bifurcated deep network for top-down contour detection
G. Bertasius, J. Shi, and L. Torresani · 2015
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Semantic image segmentation with deep convolutional nets and fully connected crfs
L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille · 2015
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Region-based convolutional networks for accurate object detection and segmentation
R. Girshick, J. Donahue, T. Darrell, and J. Malik · 2015
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Hypercolumns for object segmentation and fine-grained localization
B. Hariharan, P. Arbeláez, R. Girshick, and J. Malik · 2015
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Deeply-supervised nets
C.-Y. Lee, S. Xie, P. Gallagher, Z. Zhang, and Z. Tu · 2015
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U-net: Convolutional networks for biomedical image segmentation
O. Ronneberger, P. Fischer, and T. Brox · 2015
Squeezenet: Alexnet-level accuracy with 50x fewer parameters and < < 0.5 mb model size
F. N. Iandola, S. Han, M. W. Moskewicz, K. Ashraf, W. J. Dally, and K. Keutzer · 2016
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I. Kokkinos · 2016
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Feature space optimization for semantic video segmentation
A. Kundu, V. Vineet, and V. Koltun · 2016
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Fully convolutional networks for semantic segmentation
E. Shelhamer, J. Long, and T. Darrell · 2016
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Object contour detection with a fully convolutional encoder-decoder network
J. Yang, B. Price, S. Cohen, H. Lee, and M.-H. Yang · 2016
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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, et al · 2015
Cited alongside, same era.
DeepContour: A deep convolutional feature learned by positive-sharing loss for contour detection
W. Shen, X. Wang, Y. Wang, X. Bai, and Z. Zhang · 2015
Cited alongside, same era.
Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2015
Cited alongside, same era.
Highway networks
R. K. Srivastava, K. Greff, and J. Schmidhuber · 2015
Cited alongside, same era.
Going deeper with convolutions
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich · 2015
Cited alongside, same era.
Holistically-nested edge detection
S. Xie and Z. Tu · 2015
Cited alongside, same era.
Multi-scale context aggregation by dilated convolutions
F. Yu and V. Koltun · 2016
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S. Zagoruyko and N. Komodakis · 2016
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H. Zhao, J. Shi, X. Qi, X. Wang, and J. Jia · 2016
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Kernel pooling for convolutional neural networks
Y. Cui, F. Zhou, J. Wang, X. Liu, Y. Lin, and S. Belongie · 2017
Closest in time.
Densely connected convolutional networks
G. Huang, Z. Liu, K. Q. Weinberger, and L. van der Maaten · 2017
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Fractalnet: Ultra-deep neural networks without residuals
G. Larsson, M. Maire, and G. Shakhnarovich · 2017
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Refinenet: Multi-path refinement networks with identity mappings for high-resolution semantic segmentation
G. Lin, A. Milan, C. Shen, and I. Reid · 2017
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Feature pyramid networks for object detection
T.-Y. Lin, P. Dollár, R. Girshick, K. He, B. Hariharan, and S. Belongie · 2017
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Pascal boundaries: A semantic boundary dataset with a deep semantic boundary detector
V. Premachandran, B. Bonev, X. Lian, and A. Yuille · 2017
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Aggregated residual transformations for deep neural networks
S. Xie, R. Girshick, P. Dollár, Z. Tu, and K. He · 2017
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Dilated residual networks
F. Yu, V. Koltun, and T. Funkhouser · 2017
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