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The aim of object-centric vision is to construct an explicit representation of the objects in a scene.
Symbolism: Its meaning and effect
Alfred North Whitehead · 1928
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Orienting of attention
M. I. Posner · 1980
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Spatial extent of attention to letters and words
D. LaBerge · 1983
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Selective attention and the organization of visual information
J. Duncan · 1984
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Comparing partitions
Lawrence J. Hubert and Phipps Arabie · 1985
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Connectionism and cognitive architecture: A critical analysis
Jerry A. Fodor and Zenon W. Pylyshyn · 1988
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A connectionist model of selective attention in visual perception
M. C. Mozer · 1988
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Does visual attention select objects or locations?
S. P. Vecera and M. J. Farah · 1994
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Experience-dependent perceptual grouping and object-based attention
R. S. Zemel, M. Behrmann, M. C. Mozer, and D. Bavelier · 2002
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Core knowledge
Elizabeth S. Spelke and Katherine D. Kinzler · 2007
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The caltech-ucsd birds-200-2011 dataset
Catherine Wah, Steve Branson, Peter Welinder, Pietro Perona, and Serge J. Belongie · 2011
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Novel dataset for fine-grained image categorization : Stanford dogs
Aditya Khosla, Nityananda Jayadevaprakash, Bangpeng Yao, and Li Fei-Fei · 2012
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Estimating or propagating gradients through stochastic neurons for conditional computation
Yoshua Bengio, Nicholas Léonard, and Aaron C. 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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Microsoft COCO: common objects in context
Tsung-Yi Lin, Michael Maire, Serge J. Belongie, Lubomir D. Bourdev, Ross B. Girshick, James Hays, Pietro Perona, Deva Ramanan, Piotr Doll’a r, 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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Multiscale combinatorial grouping for image segmentation and object proposal generation
Jordi Pont-Tuset, Pablo Arbeláez, Jonathan T. Barron, Ferran Marqués, and Jitendra Malik · 2015
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Attend, infer, repeat: Fast scene understanding with generative models
S. M. Ali Eslami, Nicolas Heess, Theophane Weber, Yuval Tassa, David Szepesvari, Koray Kavukcuoglu, and Geoffrey E. Hinton · 2016
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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 B. Girshick · 2016
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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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Neural expectation maximization
Klaus Greff, Sjoerd van Steenkiste, and Jürgen Schmidhuber · 2017
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Neural discrete representation learning
Aäron van den Oord, Oriol Vinyals, and Koray Kavukcuoglu · 2017
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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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Yaniv Benny and Lior Wolf · 2019
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Monet: Unsupervised scene decomposition and representation, 2019
Systematic evaluation of causal discovery in visual model based reinforcement learning
Nan Rosemary Ke, Aniket Didolkar, Sarthak Mittal, Anirudh Goyal, Guillaume Lajoie, Stefan Bauer, Danilo Rezende, Yoshua Bengio, Michael Mozer, and Christopher Pal · 2021
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexander Kolesnikov, Alexey Dosovitskiy, Dirk Weissenborn, Georg Heigold, Jakob Uszkoreit, Lucas Beyer, Matthias Minderer, Mostafa Dehghani, Neil Houlsby, Sylvain Gelly, Thomas Unterthiner, and Xiaohua Zhai · 2021
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Toward causal representation learning
B. Schölkopf, F. Locatello, S. Bauer, N. R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio · 2021
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Benchmarking unsupervised object representations for video sequences
Marissa A. Weis, Kashyap Chitta, Yash Sharma, Wieland Brendel, Matthias Bethge, Andreas Geiger, and Alexander S. Ecker · 2021
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Discorying object that can move
Zhipeng Bao, Pavel Tokmakov, Allan Jabri, Yu-Xiong Wang, Adrien Gaidon, and Martial Hebert · 2022
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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, Chris Burgess, Daniel Zoran, Loic Matthey, Matthew M. Botvinick, and Alexander Lerchner · 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
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Spatial broadcast decoder: A simple architecture for learning disentangled representations in vaes
Nicholas Watters, Loïc Matthey, Christopher P. Burgess, and Alexander Lerchner · 2019
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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 · 2020
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Systematicity emerges in transformers when abstract grammatical roles guide attention
Ayush K Chakravarthy, Jacob Labe Russin, and Randall O’Reilly · 2022
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Michael Chang, Thomas L. Griffiths, and Sergey Levine · 2022
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Generalization and robustness implications in object-centric learning
Andrea Dittadi, Samuele S Papa, Michele De Vita, Bernhard Schölkopf, Ole Winther, and Francesco Locatello · 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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Inductive biases for deep learning of higher-level cognition
Anirudh Goyal and Yoshua Bengio · 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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Promising or elusive? unsupervised object segmentation from real-world single images
Yafei Yang and Bo Yang · 2022
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Object discovery from motion-guided tokens
Zhipeng Bao, Pavel Tokmakov, Yu-Xiong Wang, Adrien Gaidon, and Martial Hebert · 2023
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Invariant slot attention: Object discovery with slot-centric reference frames
Ondrej Biza, Sjoerd van Steenkiste, Mehdi S. M. Sajjadi, Gamaleldin F. Elsayed, Aravindh Mahendran, and Thomas Kipf · 2023
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Improving object-centric learning with query optimization
Baoxiong Jia, Yu Liu, and Siyuan Huang · 2023
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Shepherding slots to objects: Towards stable and robust object-centric learning
Jinwoo Kim, Janghyuk Choi, Ho-Jin Choi, and Seonhoon Kim · 2023
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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, and Francesco Locatello · 2023
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Learning what and where: Disentangling location and identity tracking without supervision, 2023
Manuel Traub, Sebastian Otte, Tobias Menge, Matthias Karlbauer, Jannik Thümmel, and Martin V. Butz · 2023
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