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With recent progress in graphics, it has become more tractable to train models on synthetic images, potentially avoiding the need for expensive annotations.
Learning methods for generic object recognition with invariance to pose and lighting
Y. LeCun, F. Huang, and L. Bottou · 2004
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ImageNet: A Large-Scale Hierarchical Image Database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
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Cg2real: Improving the realism of computer generated images using a large collection of photographs
M. K. Johnson, K. Dale, S. Avidan, H. Pfister, W. T. Freeman, and W. Matusik · 2011
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Articulated people detection and pose estimation: Reshaping the future
L. Pishchulin, A. Jain, M. Andriluka, T. Thormählen, and B. Schiele · 2012
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Efficient human pose estimation from single depth images
J. Shotton, R. Girshick, A. Fitzgibbon, T. Sharp, M. Cook, M. Finocchio, R. Moore, P. Kohli, A. Criminisi, A. Kipman, and A. Blake · 2013
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Unsupervised domain adaptation by backpropagation
Y. Ganin and V. Lempitsky · 2014
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Generative adversarial nets
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2014
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Learning rich features from rgb-d images for object detection and segmentation
S. Gupta, R. Girshick, P. Arbeláez, and J. Malik · 2014
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Human3.6m: Large scale datasets and predictive methods for 3d human sensing in natural environments
C. Ionescu, D. Papava, V. Olaru, and C. Sminchisescu · 2014
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Microsoft COCO: Common objects in context
T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick · 2014
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Adaptive linear regression for appearance-based gaze estimation
F. Lu, Y. Sugano, T. Okabe, and Y. Sato · 2014
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Manifold alignment for person independent appearance-based gaze estimation
T. Schneider, B. Schauerte, and R. Stiefelhagen · 2014
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Learning-by-synthesis for appearance-based 3d gaze estimation
Y. Sugano, Y. Matsushita, and Y. Sato · 2014
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Real-time continuous pose recovery of human hands using convolutional networks
J. Tompson, M. Stein, Y. Lecun, and K. Perlin · 2014
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Fast pose estimation with parameter sensitive hashing
T. Darrell, P. Viola, and G. Shakhnarovich · 2015
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SceneNet: Understanding real world indoor scenes with synthetic data
A. Handa, V. Patraucean, V. Badrinarayanan, S. Stent, and R. Cipolla · 2015
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2015
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Unsupervised learning of visual structure using predictive generative networks
W. Lotter, G. Kreiman, and D. Cox · 2015
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Articulated pose estimation with tiny synthetic videos
D. Park and D. Ramanan · 2015
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Learning deep object detectors from 3d models
X. Peng, B. Sun, K. Ali, and K. Saenko · 2015
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Depth-based hand pose estimation: data, methods, and challenges
J. Supancic, G. Rogez, Y. Yang, J. Shotton, and D. Ramanan · 2015
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Deepfont: Identify your font from an image
Z. Wang, J. Yang, H. Jin, E. Shechtman, A. Agarwala, J. Brandt, and T. Huang · 2015
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Learning classifiers from synthetic data using a multichannel autoencoder
Coupled generative adversarial networks
M.-Y. Liu and O. Tuzel · 2016
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Modeling context between objects for referring expression understanding
V. K. Nagaraja, V. I. Morariu, and L. S. Davis · 2016
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Stacked hourglass networks for human pose estimation
A. Newell, K. Yang, and J. Deng · 2016
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UnrealCV: Connecting computer vision to Unreal Engine
W. Qiu and A. Yuille · 2016
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MoCap-guided data augmentation for 3d pose estimation in the wild
G. Rogez and C. Schmid · 2016
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X. Zhang, Y. Fu, A. Zang, L. Sigal, and G. Agam · 2015
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Appearance-based gaze estimation in the wild
X. Zhang, Y. Sugano, M. Fritz, and A. Bulling · 2015
Cited alongside, same era.
Youtube-8m: A large-scale video classification benchmark
S. Abu-El-Haija, N. Kothari, J. Lee, P. Natsev, G. Toderici, B. Varadarajan, and S. Vijayanarasimhan · 2016
Cited alongside, same era.
InfoGAN: Interpretable representation learning by information maximizing generative adversarial nets
X. Chen, Y. Duan, R. Houthooft, J. Schulman, I. Sutskever, and P. Abbeel · 2016
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Virtual worlds as proxy for multi-object tracking analysis
A. Gaidon, Q. Wang, Y. Cabon, and E. Vig · 2016
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Image style transfer using convolutional neural networks
L. Gatys, A. Ecker, and M. Bethge · 2016
Cited alongside, same era.
Synthetic data for text localisation in natural images
A. Gupta, A. Vedaldi, and A. Zisserman · 2016
Cited alongside, same era.
The SYNTHIA Dataset: A large collection of synthetic images for semantic segmentation of urban scenes
G. Ros, L. Sellart, J. Materzynska, D. Vazquez, and A. M. Lopez · 2016
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Improved techniques for training gans
T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford, and X. Chen · 2016
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Play and learn: Using video games to train computer vision models
A. Shafaei, J. Little, and M. Schmidt · 2016
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Global-local face upsampling network
O. Tuzel, Y. Taguchi, and J. Hershey · 2016
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Pixel recurrent neural networks
A. van den Oord, N. Kalchbrenner, and K. Kavukcuoglu · 2016
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Generative image modeling using style and structure adversarial networks
X. Wang and A. Gupta · 2016
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Learning an appearance-based gaze estimator from one million synthesised images
E. Wood, T. Baltrušaitis, L. Morency, P. Robinson, and A. Bulling · 2016
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Pixel-level domain transfer
D. Yoo, N. Kim, S. Park, A. Paek, and I. Kweon · 2016
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Seqgan: Sequence generative adversarial nets with policy gradient
L. Yu, W. Zhang, J. Wang, and Y. Yu · 2016
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Augmenting supervised neural networks with unsupervised objectives for large-scale image classification
Y. Zhang, K. Lee, and H. Lee · 2016
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Generative visual manipulation on the natural image manifold
J.-Y. Zhu, P. Krähenbühl, E. Shechtman, and A. Efros · 2016
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