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We present a novel approach to image manipulation and understanding by simultaneously learning to segment object masks, paste objects to another background image, and remove them from original images.
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Practical automatic background substitution for live video
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Unsupervised image-to-image translation networks
M.-Y. Liu, T. Breuel, and J. Kautz · 2017
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High-resolution image synthesis and semantic manipulation with conditional gans
T.-C. Wang, M.-Y. Liu, J.-Y. Zhu, A. Tao, J. Kautz, and B. Catanzaro · 2017
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W-net: A deep model for fully unsupervised image segmentation
X. Xia and B. Kulis · 2017
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Texturegan: Controlling deep image synthesis with texture patches
W. Xian, P. Sangkloy, J. Lu, C. Fang, F. Yu, and J. Hays · 2017
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Deep image matting
N. Xu, B. L. Price, S. Cohen, and T. S. Huang · 2017
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A. Almahairi, S. Rajeswar, A. Sordoni, P. Bachman, and A. Courville · 2018
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Using simulation and domain adaptation to improve efficiency of deep robotic grasping
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Invariant information distillation for unsupervised image segmentation and clustering
X. Ji, J. F. Henriques, and A. Vedaldi · 2018
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Learning to segment via cut-and-paste
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Mattnet: Modular attention network for referring expression comprehension
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Deep unsupervised saliency detection: A multiple noisy labeling perspective
J. Zhang, T. Zhang, Y. Dai, M. Harandi, and R. I. Hartley · 2018
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