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Instance segmentation is a problem of significance in computer vision.
Interactive furniture layout using interior design guidelines
Merrell, P., Schkufza, E., Li, Z., Agrawala, M., Koltun, V.: · 2011
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Example-based synthesis of 3d object arrangements
Fisher, M., Ritchie, D., Savva, M., Funkhouser, T., Hanrahan, P.: · 2012
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The PASCAL Visual Object Classes Challenge 2012 (VOC2012) Results
Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: · 2012
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Microsoft coco: Common objects in context
Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L.: · 2014
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Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: · 2014
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Mve-a multi-view reconstruction environment
Fuhrmann, S., Langguth, F., Goesele, M.: · 2014
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Depth map prediction from a single image using a multi-scale deep network
Eigen, D., Puhrsch, C., Fergus, R.: · 2014
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Adam: A method for stochastic optimization
Kingma, D.P., Ba, J.: · 2014
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Faster R-CNN: Towards real-time object detection with region proposal networks
Ren, S., He, K., Girshick, R., Sun, J.: · 2015
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Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., Darrell, T.: · 2015
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Towards adapting deep visuomotor representations from simulated to real environments
Tzeng, E., Devin, C., Hoffman, J., Finn, C., Peng, X., Levine, S., Saenko, K., Darrell, T.: · 2015
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Render for cnn: Viewpoint estimation in images using cnns trained with rendered 3d model views
Su, H., Qi, C.R., Li, Y., Guibas, L.J.: · 2015
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3d object reconstruction from hand-object interactions
Tzionas, D., Gall, J.: · 2015
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U-net: Convolutional networks for biomedical image segmentation
Ronneberger, O., Fischer, P., Brox, T.: · 2015
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Instance-aware semantic segmentation via multi-task network cascades
Dai, J., He, K., Sun, J.: · 2016
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The cityscapes dataset for semantic urban scene understanding
Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., Schiele, B.: · 2016
Cited alongside, same era.
Target-driven visual navigation in indoor scenes using deep reinforcement learning
Zhu, Y., Mottaghi, R., Kolve, E., Lim, J.J., Gupta, A., Fei-Fei, L., Farhadi, A.: · 2016
Cited alongside, same era.
Sim-to-real robot learning from pixels with progressive nets
Rusu, A.A., Vecerik, M., Rothörl, T., Heess, N., Pascanu, R., Hadsell, R.: · 2016
Cited alongside, same era.
Learning a probabilistic latent space of object shapes via 3d generative-adversarial modeling
Wu, J., Zhang, C., Xue, T., Freeman, B., Tenenbaum, J.: · 2016
Cited alongside, same era.
Unsupervised cross-domain image generation
Taigman, Y., Polyak, A., Wolf, L.: · 2016
Cited alongside, same era.
Estimations of object frequency are frequently overestimated
Greene, M.R.: · 2016
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Fully convolutional instance-aware semantic segmentation
Li, Y., Qi, H., Dai, J., Ji, X., Wei, Y.: · 2017
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He, K., Gkioxari, G., Dollár, P., Girshick, R.: · 2017
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Unpaired image-to-image translation using cycle-consistent adversarial networks
Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: · 2017
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Image-to-image translation with conditional adversarial networks
Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: · 2017
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Sixt, L., Wild, B., Landgraf, T.: · 2016
Cited alongside, same era.
The SYNTHIA Dataset: A large collection of synthetic images for semantic segmentation of urban scenes
Ros, G., Sellart, L., Materzynska, J., Vazquez, D., Lopez, A.: · 2016
Cited alongside, same era.
Scenenet: An annotated model generator for indoor scene understanding
Handa, A., Pătrăucean, V., Stent, S., Cipolla, R.: · 2016
Cited alongside, same era.
Semantic scene completion from a single depth image
Song, S., Yu, F., Zeng, A., Chang, A.X., Savva, M., Funkhouser, T.: · 2016
Cited alongside, same era.
Chrono: An open source multi-physics dynamics engine
Tasora, A., Serban, R., Mazhar, H., Pazouki, A., Melanz, D., Fleischmann, J., Taylor, M., Sugiyama, H., Negrut, D.: · 2016
Cited alongside, same era.
Least squares generative adversarial networks
Mao, X., Li, Q., Xie, H., Lau, R.Y.K., Wang, Z., Smolley, S.P.: · 2016
Cited alongside, same era.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., Sun, J.: · 2016
Cited alongside, same era.
Chen, Q., Koltun, V.: · 2017
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Dualgan: Unsupervised dual learning for image-to-image translation
Yi, Z., Zhang, H., Gong, P.T., et al.: · 2017
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Learning to discover cross-domain relations with generative adversarial networks
Kim, T., Cha, M., Kim, H., Lee, J., Kim, J.: · 2017
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One-sided unsupervised domain mapping
Benaim, S., Wolf, L.: · 2017
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Learning from simulated and unsupervised images through adversarial training
Shrivastava, A., Pfister, T., Tuzel, O., Susskind, J., Wang, W., Webb, R.: · 2017
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Unsupervised pixel-level domain adaptation with generative adversarial networks
Bousmalis, K., Silberman, N., Dohan, D., Erhan, D., Krishnan, D.: · 2017
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Synthesizing training data for object detection in indoor scenes
Georgakis, G., Mousavian, A., Berg, A.C., Kosecka, J.: · 2017
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Augmented reality meets deep learning for car instance segmentation in urban scenes
Alhaija, H.A., Mustikovela, S.K., Mescheder, L., Geiger, A., Rother, C.: · 2017
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Scenenet rgb-d: 5m photorealistic images of synthetic indoor trajectories with ground truth
Mccormac, J., Handa, A., Leutenegger, S., Davison, A.J.: · 2017
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