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Object-centric learning (OCL) aspires general and compositional understanding of scenes by representing a scene as a collection of object-centric representations.
The hungarian method for the assignment problem
Harold W Kuhn · 1955
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Objective criteria for the evaluation of clustering methods
William M Rand · 1971
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Machine vision: Automated visual inspection and robot vision
David Vernon · 1991
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The reviewing of object files: Object-specific integration of information
Daniel Kahneman, Anne Treisman, and Brian J Gibbs · 1992
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Multiple view geometry in computer vision
Richard Hartley and Andrew Zisserman · 2003
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Core knowledge
Elizabeth S Spelke and Katherine D Kinzler · 2007
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Unsupervised object discovery and localization in the wild: Part-based matching with bottom-up region proposals
Minsu Cho, Suha Kwak, Cordelia Schmid, and Jean Ponce · 2015
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Weakly-and semi-supervised learning of a deep convolutional network for semantic image segmentation
George Papandreou, Liang-Chieh Chen, Kevin P Murphy, and Alan L Yuille · 2015
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Attend, infer, repeat: Fast scene understanding with generative models
SM Eslami, Nicolas Heess, Theophane Weber, Yuval Tassa, David Szepesvari, Geoffrey E Hinton, et al · 2016
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Tagger: Deep unsupervised perceptual grouping
Klaus Greff, Antti Rasmus, Mathias Berglund, Tele Hao, Harri Valpola, and Jürgen Schmidhuber · 2016
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Temporal ensembling for semi-supervised learning
Samuli Laine and Timo Aila · 2016
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Neural expectation maximization
Klaus Greff, Sjoerd Van Steenkiste, and Jürgen Schmidhuber · 2017
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Learning by association–a versatile semi-supervised training method for neural networks
Philip Haeusser, Alexander Mordvintsev, and Daniel Cremers · 2017
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Clevr: A diagnostic dataset for compositional language and elementary visual reasoning
Justin Johnson, Bharath Hariharan, Laurens van der Maaten, Li Fei-Fei, C Lawrence Zitnick, and Ross Girshick · 2017
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Simple does it: Weakly supervised instance and semantic segmentation
Anna Khoreva, Rodrigo Benenson, Jan Hosang, Matthias Hein, and Bernt Schiele · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Learning pixel-level semantic affinity with image-level supervision for weakly supervised semantic segmentation
Jiwoon Ahn and Suha Kwak · 2018
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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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Mixmatch: A holistic approach to semi-supervised learning
David Berthelot, Nicholas Carlini, Ian Goodfellow, Nicolas Papernot, Avital Oliver, and Colin A Raffel · 2019
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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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Spatially invariant unsupervised object detection with convolutional neural networks
Eric Crawford and Joelle Pineau · 2019
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Multi-object representation learning with iterative variational inference
Klaus Greff, Raphaël Lopez Kaufman, Rishabh Kabra, Nick Watters, Christopher Burgess, Daniel Zoran, Loic Matthey, Matthew Botvinick, and Alexander Lerchner · 2019
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Consistency-based semi-supervised learning for object detection
Jisoo Jeong, Seungeui Lee, Jeesoo Kim, and Nojun Kwak · 2019
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Multi-object datasets
Rishabh Kabra, Chris Burgess, Loic Matthey, Raphael Lopez Kaufman, Klaus Greff, Malcolm Reynolds, and Alexander Lerchner · 2019
Cited alongside, same era.
Ptr: A benchmark for part-based conceptual, relational, and physical reasoning
Yining Hong, Li Yi, Joshua B Tenenbaum, Antonio Torralba, and Chuang Gan · 2021
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Simone: View-invariant, temporally-abstracted object representations via unsupervised video decomposition
Rishabh Kabra, Daniel Zoran, Goker Erdogan, Loic Matthey, Antonia Creswell, Matt Botvinick, Alexander Lerchner, and Chris Burgess · 2021
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Clevrtex: A texture-rich benchmark for unsupervised multi-object segmentation
Laurynas Karazija, Iro Laina, and Christian Rupprecht · 2021
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Semi-supervised semantic segmentation with directional context-aware consistency
Xin Lai, Zhuotao Tian, Li Jiang, Shu Liu, Hengshuang Zhao, Liwei Wang, and Jiaya Jia · 2021
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Unsupervised layered image decomposition into object prototypes
Tom Monnier, Elliot Vincent, Jean Ponce, and Mathieu Aubry · 2021
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The neuro-symbolic concept learner: Interpreting scenes, words, and sentences from natural supervision
Jiayuan Mao, Chuang Gan, Pushmeet Kohli, Joshua B Tenenbaum, and Jiajun Wu · 2019
Cited alongside, same era.
Faster attend-infer-repeat with tractable probabilistic models
Karl Stelzner, Robert Peharz, and Kristian Kersting · 2019
Cited alongside, same era.
Spatial broadcast decoder: A simple architecture for learning disentangled representations in vaes
Nicholas Watters, Loic Matthey, Christopher P Burgess, and Alexander Lerchner · 2019
Cited alongside, same era.
Genesis: Generative scene inference and sampling with object-centric latent representations
Martin Engelcke, Adam R Kosiorek, Oiwi Parker Jones, and Ingmar Posner · 2020
Cited alongside, same era.
Cater: A diagnostic dataset for compositional actions and temporal reasoning
Rohit Girdhar and Deva Ramanan · 2020
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Unsupervised object-centric video generation and decomposition in 3D
Paul Henderson and Christoph H. Lampert · 2020
Cited alongside, same era.
Scalor: Generative world models with scalable object representations
Jindong Jiang, Sepehr Janghorbani, Gerard De Melo, and Sungjin Ahn · 2020
Cited alongside, same era.
Youngtaek Oh, Dong-Jin Kim, and In So Kweon · 2021
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Illiterate dall-e learns to compose
Gautam Singh, Fei Deng, and Sungjin Ahn · 2021
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Decomposing 3d scenes into objects via unsupervised volume segmentation
Karl Stelzner, Kristian Kersting, and Adam R Kosiorek · 2021
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End-to-end semi-supervised object detection with soft teacher
Mengde Xu, Zheng Zhang, Han Hu, Jianfeng Wang, Lijuan Wang, Fangyun Wei, Xiang Bai, and Zicheng Liu · 2021
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Weakly supervised learning of multi-object 3d scene decompositions using deep shape priors
Cathrin Elich, Martin R Oswald, Marc Pollefeys, and Joerg Stueckler · 2022
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Savi++: Towards end-to-end object-centric learning from real-world videos
Gamaleldin F Elsayed, Aravindh Mahendran, Sjoerd van Steenkiste, Klaus Greff, Michael C Mozer, and Thomas Kipf · 2022
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Kubric: a scalable dataset generator
Klaus Greff, Francois Belletti, Lucas Beyer, Carl Doersch, Yilun Du, Daniel Duckworth, David J Fleet, Dan Gnanapragasam, Florian Golemo, Charles Herrmann, Thomas Kipf, Abhijit Kundu, Dmitry Lagun, Issam Laradji, Hsueh-Ti (Derek) Liu, Henning Meyer, Yishu Miao, Derek Nowrouzezahrai, Cengiz Oztireli, Etienne Pot, Noha Radwan, Daniel Rebain, Sara Sabour, Mehdi S. M. Sajjadi, Matan Sela, Vincent Sitzmann, Austin Stone, Deqing Sun, Suhani Vora, Ziyu Wang, Tianhao Wu, Kwang Moo Yi, Fangcheng Zhong, and Andrea Tagliasacchi · 2022
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Conditional object-centric learning from video
Thomas Kipf, Gamaleldin F Elsayed, Aravindh Mahendran, Austin Stone, Sara Sabour, Georg Heigold, Rico Jonschkowski, Alexey Dosovitskiy, and Klaus Greff · 2022
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Generating fast and slow: Scene decomposition via reconstruction
Mihir Prabhudesai, Anirudh Goyal, Deepak Pathak, and Katerina Fragkiadaki · 2022
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Object scene representation transformer
Mehdi SM Sajjadi, Daniel Duckworth, Aravindh Mahendran, Sjoerd van Steenkiste, Filip Pavetić, Mario Lučić, Leonidas J Guibas, Klaus Greff, and Thomas Kipf · 2022
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Scene representation transformer: Geometry-free novel view synthesis through set-latent scene representations
Mehdi SM Sajjadi, Henning Meyer, Etienne Pot, Urs Bergmann, Klaus Greff, Noha Radwan, Suhani Vora, Mario Lučić, Daniel Duckworth, Alexey Dosovitskiy, et al · 2022
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Bridging the gap to real-world object-centric learning
Maximilian Seitzer, Max Horn, Andrii Zadaianchuk, Dominik Zietlow, Tianjun Xiao, Carl-Johann Simon-Gabriel, Tong He, Zheng Zhang, Bernhard Schölkopf, Thomas Brox, et al · 2022
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Simple unsupervised object-centric learning for complex and naturalistic videos
Gautam Singh, Yi-Fu Wu, and Sungjin Ahn · 2022
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