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We study the problem of unsupervised physical object discovery.
Perception of partly occluded objects in infancy
Philip J Kellman and Elizabeth S Spelke · 1983
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Origins of knowledge
Elizabeth S Spelke, Karen Breinlinger, Janet Macomber, and Kristen Jacobson · 1992
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Normalized cuts and image segmentation
Jianbo Shi and Jitendra Malik · 2000
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Core knowledge
Elizabeth S Spelke and Katherine D Kinzler · 2007
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Object segmentation by long term analysis of point trajectories
Thomas Brox and Jitendra Malik · 2010
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Toward object discovery and modeling via 3-d scene comparison
Evan Herbst, Peter Henry, Xiaofeng Ren, and Dieter Fox · 2011
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Object discovery in 3d scenes via shape analysis
Andrej Karpathy, Stephen Miller, and Li Fei-Fei · 2013
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Crisp boundary detection using pointwise mutual information
Phillip Isola, Daniel Zoran, Dilip Krishnan, and Edward H Adelson · 2014
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Auto-encoding variational bayes
Diederik P. Kingma and Max Welling · 2014
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Semi-supervised learning with deep generative models
Diederik P Kingma, Shakir Mohamed, Danilo Jimenez Rezende, and Max Welling · 2014
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Unsupervised dense object discovery, detection, tracking and reconstruction
Lu Ma and Gabe Sibley · 2014
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Stochastic backpropagation and approximate inference in deep generative models
Danilo J Rezende, Shakir Mohamed, and Daan Wierstra · 2014
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Shapenet: An information-rich 3d model repository
Angel X Chang, Thomas Funkhouser, Leonidas Guibas, Pat Hanrahan, Qixing Huang, Zimo Li, Silvio Savarese, Manolis Savva, Shuran Song, Hao Su, Jianxiong Xiao, Li Yi, and Fisher Yu · 2015
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Learning to segment moving objects in videos
Katerina Fragkiadaki, Pablo Arbelaez, Panna Felsen, and Jitendra Malik · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
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Attend, infer, repeat: Fast scene understanding with generative models
SM Eslami, Nicolas Heess, Theophane Weber, Yuval Tassa, Koray Kavukcuoglu, and Geoffrey E Hinton · 2016
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Multiview rgb-d dataset for object instance detection
Georgios Georgakis, Md Alimoor Reza, Arsalan Mousavian, Phi-Hung Le, and Jana Košecká · 2016
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Learning physical intuition of block towers by example
Adam Lerer, Sam Gross, and Rob Fergus · 2016
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Unsupervised learning of 3d structure from images
Danilo Jimenez Rezende, SM Eslami, Shakir Mohamed, Peter Battaglia, Max Jaderberg, and Nicolas Heess · 2016
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Perspective transformer nets: Learning single-view 3d object reconstruction without 3d supervision
Xinchen Yan, Jimei Yang, Ersin Yumer, Yijie Guo, and Honglak Lee · 2016
Cited alongside, same era.
Neural expectation maximization
Klaus Greff, Sjoerd van Steenkiste, and Jürgen Schmidhuber · 2017
Relational neural expectation maximization: Unsupervised discovery of objects and their interactions
Sjoerd van Steenkiste, Michael Chang, Klaus Greff, and Jürgen Schmidhuber · 2018
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Monet: Unsupervised scene decomposition and representation
Christopher P Burgess, Loic Matthey, Nicholas Watters, Rishabh Kabra, Irina Higgins, Matt Botvinick, and Alexander Lerchner · 2019
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Towards segmenting anything that moves
Achal Dave, Pavel Tokmakov, and Deva Ramanan · 2019
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Multi-Object Representation Learning with Iterative Variational Inference
Klaus Greff, Raphaël Lopez Kaufman, Rishabh Kabra, Nick Watters, Chris Burgess, Daniel Zoran, Loic Matthey, Matthew Botvinick, and Alexander Lerchner · 2019
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See more, know more: Unsupervised video object segmentation with co-attention siamese networks
Xiankai Lu, Wenguan Wang, Chao Ma, Jianbing Shen, Ling Shao, and Fatih Porikli · 2019
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Cited alongside, same era.
Schema networks: Zero-shot transfer with a generative causal model of intuitive physics
Ken Kansky, Tom Silver, David A Mély, Mohamed Eldawy, Miguel Lázaro-Gredilla, Xinghua Lou, Nimrod Dorfman, Szymon Sidor, Scott Phoenix, and Dileep George · 2017
Cited alongside, same era.
Learning to see physics via visual de-animation
Jiajun Wu, Erika Lu, Pushmeet Kohli, Bill Freeman, and Josh Tenenbaum · 2017
Cited alongside, same era.
The best of both worlds: Combining cnns and geometric constraints for hierarchical motion segmentation
Pia Bideau, Aruni RoyChowdhury, Rakesh R Menon, and Erik Learned-Miller · 2018
Cited alongside, same era.
Neural scene representation and rendering
SM Ali Eslami, Danilo Jimenez Rezende, Frederic Besse, Fabio Viola, Ari S Morcos, Marta Garnelo, Avraham Ruderman, Andrei A Rusu, Ivo Danihelka, Karol Gregor, et al · 2018
Cited alongside, same era.
Reasoning about physical interactions with object-oriented prediction and planning
Michael Janner, Sergey Levine, William T Freeman, Joshua B Tenenbaum, Chelsea Finn, and Jiajun Wu · 2018
Cited alongside, same era.
Sequential attend, infer, repeat: Generative modelling of moving objects
Adam Kosiorek, Hyunjik Kim, Yee Whye Teh, and Ingmar Posner · 2018
Cited alongside, same era.
Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
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Scene representation networks: Continuous 3d-structure-aware neural scene representations
Vincent Sitzmann, Michael Zollhöfer, and Gordon Wetzstein · 2019
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Modeling expectation violation in intuitive physics with coarse probabilistic object representations
Kevin Smith, Lingjie Mei, Shunyu Yao, Jiajun Wu, Elizabeth Spelke, Josh Tenenbaum, and Tomer Ullman · 2019
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R-sqair: Relational sequential attend, infer, repeat
Aleksandar Stanić and Jürgen Schmidhuber · 2019
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Entity abstraction in visual model-based reinforcement learning
Rishi Veerapaneni, John D Co-Reyes, Michael Chang, Michael Janner, Chelsea Finn, Jiajun Wu, Joshua B Tenenbaum, and Sergey Levine · 2019
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Zero-shot video object segmentation via attentive graph neural networks
Wenguan Wang, Xiankai Lu, Jianbing Shen, David J Crandall, and Ling Shao · 2019
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Object discovery in videos as foreground motion clustering
Christopher Xie, Yu Xiang, Zaid Harchaoui, and Dieter Fox · 2019
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Dops: Learning to detect 3d objects and predict their 3d shapes
Mahyar Najibi, Guangda Lai, Abhijit Kundu, Zhichao Lu, Vivek Rathod, Thomas Funkhouser, Caroline Pantofaru, David Ross, Larry S. Davis, and Alireza Fathi · 2020
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Learning to simulate complex physics with graph networks
Alvaro Sanchez-Gonzalez, Jonathan Godwin, Tobias Pfaff, Rex Ying, Jure Leskovec, and Peter Battaglia · 2020
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