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The ability to decompose complex natural scenes into meaningful object-centric abstractions lies at the core of human perception and reasoning.
Symbolism: Its meaning and effect
Alfred North Whitehead · 1928
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Comparing partitions
Lawrence Hubert and Phipps Arabie · 1985
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Core knowledge
Elizabeth S Spelke and Katherine D Kinzler · 2007
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The graph neural network model
Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini · 2008
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Visualizing data using t-sne
Laurens Van der Maaten and Geoffrey Hinton · 2008
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Delving deeper into the whorl of flower segmentation
Maria-Elena Nilsback and Andrew Zisserman · 2010
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Caltech-ucsd birds 200
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Novel dataset for fine-grained image categorization: Stanford dogs
Aditya Khosla, Nityananda Jayadevaprakash, Bangpeng Yao, and Fei-Fei Li · 2011
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Estimating or propagating gradients through stochastic neurons for conditional computation
Yoshua Bengio, Nicholas Léonard, and Aaron Courville · 2013
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3d object representations for fine-grained categorization
Jonathan Krause, Michael Stark, Jia Deng, and Li Fei-Fei · 2013
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Learning phrase representations using rnn encoder-decoder for statistical machine translation
Kyunghyun Cho, Bart Van Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio · 2014
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Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 2014
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Draw: A recurrent neural network for image generation
Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Rezende, and Daan Wierstra · 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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Hyperparameter optimization with approximate gradient
Fabian Pedregosa · 2016
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Optnet: Differentiable optimization as a layer in neural networks
Brandon Amos and J Zico Kolter · 2017
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Yale-cmu-berkeley dataset for robotic manipulation research
Berk Calli, Arjun Singh, James Bruce, Aaron Walsman, Kurt Konolige, Siddhartha Srinivasa, Pieter Abbeel, and Aaron M Dollar · 2017
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Scannet: Richly-annotated 3d reconstructions of indoor scenes
Angela Dai, Angel X Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, and Matthias Nießner · 2017
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Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
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Neural expectation maximization
Klaus Greff, Sjoerd Van Steenkiste, and Jürgen Schmidhuber · 2017
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Neural discrete representation learning
Aaron Van Den Oord, Oriol Vinyals, et al · 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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Deep sets
Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barnabas Poczos, Russ R Salakhutdinov, and Alexander J Smola · 2017
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Bilevel programming for hyperparameter optimization and meta-learning
Luca Franceschi, Paolo Frasconi, Saverio Salzo, Riccardo Grazzi, and Massimiliano Pontil · 2018
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Shapestacks: Learning vision-based physical intuition for generalised object stacking
Oliver Groth, Fabian B Fuchs, Ingmar Posner, and Andrea Vedaldi · 2018
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Reptile: a scalable metalearning algorithm
Alex Nichol and John Schulman · 2018
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Deep equilibrium models
Shaojie Bai, J Zico Kolter, and Vladlen Koltun · 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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Unsupervised object segmentation by redrawing
Mickaël Chen, Thierry Artières, and Ludovic Denoyer · 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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Scalor: Generative world models with scalable object representations
Jindong Jiang, Sepehr Janghorbani, Gerard De Melo, and Sungjin Ahn · 2019
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Genesis-v2: Inferring unordered object representations without iterative refinement
Martin Engelcke, Oiwi Parker Jones, and Ingmar Posner · 2021
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On training implicit models
Zhengyang Geng, Xin-Yu Zhang, Shaojie Bai, Yisen Wang, and Zhouchen Lin · 2021
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Recurrent independent mechanisms
Anirudh Goyal, Alex Lamb, Jordan Hoffmann, Shagun Sodhani, Sergey Levine, Yoshua Bengio, and Bernhard Schölkopf · 2021
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Ptr: A benchmark for part-based conceptual, relational, and physical reasoning
Yining Hong, Li Yi, Josh Tenenbaum, Antonio Torralba, and Chuang Gan · 2021
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Transformers with competitive ensembles of independent mechanisms
Alex Lamb, Di He, Anirudh Goyal, Guolin Ke, Chien-Feng Liao, Mirco Ravanelli, and Yoshua Bengio · 2021
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Multi-object datasets
Rishabh Kabra, Chris Burgess, Loic Matthey, Raphael Lopez Kaufman, Klaus Greff, Malcolm Reynolds, and Alexander Lerchner · 2019
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Set transformer: A framework for attention-based permutation-invariant neural networks
Juho Lee, Yoonho Lee, Jungtaek Kim, Adam Kosiorek, Seungjin Choi, and Yee Whye Teh · 2019
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Meta-learning with implicit gradients
Aravind Rajeswaran, Chelsea Finn, Sham M Kakade, and Sergey Levine · 2019
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Truncated back-propagation for bilevel optimization
Amirreza Shaban, Ching-An Cheng, Nathan Hatch, and Byron Boots · 2019
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Spatial broadcast decoder: A simple architecture for learning disentangled representations in vaes
Nicholas Watters, Loic Matthey, Christopher P Burgess, and Alexander Lerchner · 2019
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Deep set prediction networks
Yan Zhang, Jonathon Hare, and Adam Prugel-Bennett · 2019
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Learning physical graph representations from visual scenes
Daniel Bear, Chaofei Fan, Damian Mrowca, Yunzhu Li, Seth Alter, Aran Nayebi, Jeremy Schwartz, Li F Fei-Fei, Jiajun Wu, Josh Tenenbaum, et al · 2020
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Kanika Madan, Nan Rosemary Ke, Anirudh Goyal, Bernhard Schölkopf, and Yoshua Bengio · 2021
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Finding an unsupervised image segmenter in each of your deep generative models
Luke Melas-Kyriazi, Christian Rupprecht, Iro Laina, and Andrea Vedaldi · 2021
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Nerf: Representing scenes as neural radiance fields for view synthesis
Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng · 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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Zero-shot text-to-image generation
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Toward causal representation learning
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Illiterate dall-e learns to compose
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Self-supervised video object segmentation by motion grouping
Charig Yang, Hala Lamdouar, Erika Lu, Andrew Zisserman, and Weidi Xie · 2021
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Unsupervised foreground extraction via deep region competition
Peiyu Yu, Sirui Xie, Xiaojian Ma, Yixin Zhu, Ying Nian Wu, and Song-Chun Zhu · 2021
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Temporal query networks for fine-grained video understanding
Chuhan Zhang, Ankush Gupta, and Andrew Zisserman · 2021
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Parts: Unsupervised segmentation with slots, attention and independence maximization
Daniel Zoran, Rishabh Kabra, Alexander Lerchner, and Danilo J Rezende · 2021
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Object representations as fixed points: Training iterative refinement algorithms with implicit differentiation
Michael Chang, Thomas L Griffiths, and Sergey Levine · 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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Refine and represent: Region-to-object representation learning
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Object discovery and representation networks
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Conditional object-centric learning from video
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Simple unsupervised object-centric learning for complex and naturalistic videos
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Self-supervised transformers for unsupervised object discovery using normalized cut
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Unsupervised discovery of object radiance fields
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