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We introduce a novel framework to build a model that can learn how to segment objects from a collection of images without any human annotation.
Unsupervised learning
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Mean shift: A robust approach toward feature space analysis
Dorin Comaniciu and Peter Meer · 2002
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Unsupervised Learning , pages 72–112
Zoubin Ghahramani · 2004
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Grabcut -interactive foreground extraction using iterated graph cuts
Carsten Rother, Vladimir Kolmogorov, and Andrew Blake · 2004
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The Caltech-UCSD Birds-200-2011 Dataset
C. Wah, S. Branson, P. Welinder, P. Perona, and S. Belongie · 2011
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Slic superpixels compared to state-of-the-art superpixel methods
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Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollar, and Larry Zitnick · 2014
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Unsupervised salient object matting
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Adam: A method for stochastic optimization
Diederick P Kingma and Jimmy Ba · 2015
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Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop
Fisher Yu, Yinda Zhang, Shuran Song, Ari Seff, and Jianxiong Xiao · 2015
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Attend, infer, repeat: Fast scene understanding with generative models
SM Ali Eslami, Nicolas Heess, Theophane Weber, Yuval Tassa, David Szepesvari, Geoffrey E Hinton, et al · 2016
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Learning to segment every thing
Ronghang Hu, Piotr Dollár, Kaiming He, Trevor Darrell, and Ross Girshick · 2018
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Invariant information clustering for unsupervised image classification and segmentation
Xu Ji, João F. Henriques, and Andrea Vedaldi · 2018
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Unsupervised image segmentation by backpropagation
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Sebastian Lutz, Konstantinos Amplianitis, and Aljosa Smolic · 2018
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Pavel Ostyakov, Roman Suvorov, Elizaveta Logacheva, Oleg Khomenko, and Sergey I. Nikolenko · 2018
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Monet: Unsupervised scene decomposition and representation
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