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The success of deep learning in computer vision is based on availability of large annotated datasets.
Blender - a 3D modelling and rendering package
Blender Online Community · 2006
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Object retrieval with large vocabularies and fast spatial matching
J. Philbin, O. Chum, M. Isard, J. Sivic, and A. Zisserman · 2007
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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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Learning people detection models from few training samples
L. Pishchulin, A. Jain, C. Wojek, M. Andriluka, T. Thormählen, and B. Schiele · 2011
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Vision meets robotics: The KITTI dataset
A. Geiger, P. Lenz, C. Stiller, and R. Urtasun · 2013
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Deepface: Closing the gap to human-level performance in face verification
Y. Taigman, M. Yang, M. Ranzato, and L. Wolf · 2014
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Flownet: Learning optical flow with convolutional networks
A. Dosovitskiy, P. Fischer, E. Ilg, P. Haeusser, C. Hazirbas, V. Golkov, P. v.d. Smagt, D. Cremers, and T. Brox · 2015
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Learning scene-specific pedestrian detectors without real data
H. Hattori, V. N. Boddeti, K. M. Kitani, and T. Kanade · 2015
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Object scene flow for autonomous vehicles
M. Menze and A. Geiger · 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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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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On rendering synthetic images for training an object detector
A. Rozantsev, V. Lepetit, and P. Fua · 2015
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2015
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Render for CNN: viewpoint estimation in images using cnns trained with rendered 3d model views
H. Su, C. R. Qi, Y. Li, and L. J. Guibas · 2015
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Synthesizing training images for boosting human 3d pose estimation
W. Chen, H. Wang, Y. Li, H. Su, Z. Wang, C. Tu, D. Lischinski, D. Cohen-Or, and B. Chen · 2016
Understanding real world indoor scenes with synthetic data
A. Handa, V. Patraucean, V. Badrinarayanan, S. Stent, and R. Cipolla · 2016
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How useful is photo-realistic rendering for visual learning?
Y. Movshovitz-Attias, T. Kanade, and Y. Sheikh · 2016
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Playing for data: Ground truth from computer games
S. R. Richter, V. Vineet, S. Roth, and V. Koltun · 2016
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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. Lopez · 2016
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Play and learn: Using video games to train computer vision models
A. Shafaei, J. J. Little, and M. Schmidt · 2016
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Multinet: Real-time joint semantic reasoning for autonomous driving
M. Teichmann, M. Weber, J. M. Zöllner, R. Cipolla, and R. Urtasun · 2016
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The cityscapes dataset for semantic urban scene understanding
M. Cordts, M. Omran, S. Ramos, T. Rehfeld, M. Enzweiler, R. Benenson, U. Franke, S. Roth, and B. Schiele · 2016
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Instance-aware semantic segmentation via multi-task network cascades
J. Dai, K. He, and J. Sun · 2016
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Procedural generation of videos to train deep action recognition networks
C. R. de Souza, A. Gaidon, Y. Cabon, and A. M. L. Peña · 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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Synthetic data for text localisation in natural images
A. Gupta, A. Vedaldi, and A. Zisserman · 2016
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Semantic instance annotation of street scenes by 3d to 2d label transfer
J. Xie, M. Kiefel, M.-T. Sun, and A. Geiger · 2016
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Unrealstereo: A synthetic dataset for analyzing stereo vision
Y. Zhang, W. Qiu, Q. Chen, X. Hu, and A. L. Yuille · 2016
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Physically-based rendering for indoor scene understanding using convolutional neural networks
Y. Zhang, S. Song, E. Yumer, M. Savva, J. Lee, H. Jin, and T. A. Funkhouser · 2016
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Target-driven visual navigation in indoor scenes using deep reinforcement learning
Y. Zhu, R. Mottaghi, E. Kolve, J. J. Lim, A. Gupta, L. Fei-Fei, and A. Farhadi · 2016
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Learning from synthetic humans
G. Varol, J. Romero, X. Martin, N. Mahmood, M. J. Black, I. Laptev, and C. Schmid · 2017
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