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We introduce a novel method for representation learning that uses an artificial supervision signal based on counting visual primitives.
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J. Dai · 2015
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C. Doersch, A. Gupta, and A. A. Efros · 2015
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Fast r-cnn
R. Girshick · 2015
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Detecting humans in dense crowds using locally-consistent scale prior and global occlusion reasoning
H. Idrees, K. Soomro, and M. Shah · 2015
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Learning image representations tied to ego-motion
D. Jayaraman and K. Grauman · 2015
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Understanding image representations by measuring their equivariance and equivalence
K. Lenc and A. Vedaldi · 2015
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Fully convolutional networks for semantic segmentation
J. Long, E. Shelhamer, and T. Darrell · 2015
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Deep visual analogy-making
S. Reed, Y. Zhang, Y. Zhang, and H. Lee · 2015
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Shuffle and learn: Unsupervised learning using temporal order verification
I. Misra, C. L. Zitnick, and M. Hebert · 2016
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A large contextual dataset for classification, detection and counting of cars with deep learning
T. N. Mundhenk, G. Konjevod, W. A. Sakla, and K. Boakye · 2016
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Unsupervised learning of visual representations by solving jigsaw puzzles
M. Noroozi and P. Favaro · 2016
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Unsupervised learning of visual representations by solving jigsaw puzzles
M. Noroozi and P. Favaro · 2016
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Ambient sound provides supervision for visual learning
A. Owens, J. Wu, J. H. M. annd William T. Freeman, and A. Torralba · 2016
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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 · 2015
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Deeply learned attributes for crowded scene understanding
J. Shao, K. Kang, C. C. Loy, and X. Wang · 2015
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Unsupervised learning of visual representations using videos
X. Wang and A. Gupta · 2015
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Cross-scene crowd counting via deep convolutional neural networks
C. Zhang, H. Li, X. Wang, and X. Yang · 2015
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Counting in the wild
C. Arteta, V. Lempitsky, and A. Zisserman · 2016
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Counting everyday objects in everyday scenes
P. Chattopadhyay, R. Vedantam, R. R. Selvaraju, D. Batra, and D. Parikh · 2016
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D. Pathak, R. Girshick, P. Dollár, T. Darrell, and B. Hariharan · 2016
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Context encoders: Feature learning by inpainting
D. Pathak, P. Krahenbuhl, J. Donahue, T. Darrell, and A. A. Efros · 2016
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The curious robot: Learning visual representations via physical interactions
L. Pinto, D. Gandhi, Y. Han, Y.-L. Park, and A. Gupta · 2016
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Unsupervised representation learning with deep convolutional generative adversarial networks
A. Radford, L. Metz, and S. Chintala · 2016
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Colorful image colorization
R. Zhang, P. Isola, and A. A. Efros · 2016
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Split-brain autoencoders: Unsupervised learning by cross-channel prediction
R. Zhang, P. Isola, and A. A. Efros · 2016
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Adversarial feature learning
J. Donahue, P. Krähenbühl, and T. Darrell · 2017
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Colorization as a proxy task for visual understanding
G. Larsson, M. Maire, and G. Shakhnarovich · 2017
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End-to-end instance segmentation with recurrent attention
M. Ren and R. S. Zemel · 2017
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