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Recently developed deep learning models are able to learn to segment scenes into component objects without supervision.
Object permanence in five-month-old infants
R. Baillargeon, E. S. Spelke, and S. Wasserman · 1985
Earlier work this paper cites.
The role of location indexes in spatial perception: A sketch of the finst spatial-index model
Z. W. Pylyshyn · 1989
Earlier work this paper cites.
Core knowledge
E. S. Spelke and K. D. Kinzler · 2007
Earlier work this paper cites.
A. Graves, G. Wayne, and I. Danihelka · 2014
Earlier work this paper cites.
Pointer networks
O. Vinyals, M. Fortunato, and N. Jaitly · 2015
Earlier work this paper cites.
Learning efficient algorithms with hierarchical attentive memory
M. Andrychowicz and K. Kurach · 2016
Earlier work this paper cites.
Neural combinatorial optimization with reinforcement learning
I. Bello, H. Pham, Q. V. Le, M. Norouzi, and S. Bengio · 2016
Earlier work this paper cites.
A compositional object-based approach to learning physical dynamics, 2016
M. B. Chang, T. Ullman, A. Torralba, and J. B. Tenenbaum · 2016
Earlier work this paper cites.
Tagger: Deep unsupervised perceptual grouping
K. Greff, A. Rasmus, M. Berglund, T. Hao, H. Valpola, and J. Schmidhuber · 2016
Earlier work this paper cites.
Data-driven approximations to np-hard problems
A. Milan, S. H. Rezatofighi, R. Garg, A. Dick, and I. Reid · 2017
Earlier work this paper cites.
The multi-entity variational autoencoder
C. Nash, A. Eslami, C. Burgess, I. Higgins, D. Zoran, T. Weber, and P. Battaglia · 2017
Earlier work this paper cites.
Neural map: Structured memory for deep reinforcement learning
E. Parisotto and R. Salakhutdinov · 2017
Earlier work this paper cites.
End-to-end representation learning for correlation filter based tracking
J. Valmadre, L. Bertinetto, J. Henriques, A. Vedaldi, and P. H. Torr · 2017
Earlier work this paper cites.
Tracking by animation: Unsupervised learning of multi-object attentive trackers
Z. He, J. Li, D. Liu, H. He, and D. Barber · 2018
Earlier work this paper cites.
Reasoning about physical interactions with object-oriented prediction and planning
M. Janner, S. Levine, W. T. Freeman, J. B. Tenenbaum, C. Finn, and J. Wu · 2018
Cited alongside, same era.
Learning latent permutations with gumbel-sinkhorn networks
G. Mena, D. Belanger, S. Linderman, and J. Snoek · 2018
Cited alongside, same era.
Probing physics knowledge using tools from developmental psychology
L. Piloto, A. Weinstein, D. TB, A. Ahuja, M. Mirza, G. Wayne, D. Amos, C.-c. Hung, and M. Botvinick · 2018
Cited alongside, same era.
Learning dynamic memory networks for object tracking
T. Yang and A. B. Chan · 2018
Cited alongside, same era.
Neural-symbolic vqa: Disentangling reasoning from vision and language understanding
K. Yi, J. Wu, C. Gan, A. Torralba, P. Kohli, and J. Tenenbaum · 2018
Modeling expectation violation in intuitive physics with coarse probabilistic object representations
K. Smith, L. Mei, S. Yao, J. Wu, E. Spelke, J. Tenenbaum, and T. Ullman · 2019
Later among the works it cites.
Entity abstraction in visual model-based reinforcement learning, 2019
R. Veerapaneni, J. D. Co-Reyes, M. Chang, M. Janner, C. Finn, J. Wu, J. B. Tenenbaum, and S. Levine · 2019
Later among the works it cites.
Spriteworld: A flexible, configurable reinforcement learning environment
N. Watters, L. Matthey, S. Borgeaud, R. Kabra, and A. Lerchner · 2019
Later among the works it cites.
Cobra: Data-efficient model-based rl through unsupervised object discovery and curiosity-driven exploration, 2019
N. Watters, L. Matthey, M. Bosnjak, C. P. Burgess, and A. Lerchner · 2019
Later among the works it cites.
Clevrer: Collision events for video representation and reasoning, 2019
K. Yi, C. Gan, Y. Li, P. Kohli, J. Wu, A. Torralba, and J. B. Tenenbaum · 2019
Later among the works it cites.
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Cited alongside, same era.
Relational deep reinforcement learning
V. Zambaldi, D. Raposo, A. Santoro, V. Bapst, Y. Li, I. Babuschkin, K. Tuyls, D. Reichert, T. Lillicrap, E. Lockhart, et al · 2018
Cited alongside, same era.
Monet: Unsupervised scene decomposition and representation
C. P. Burgess, L. Matthey, N. Watters, R. Kabra, I. Higgins, M. Botvinick, and A. Lerchner · 2019
Cited alongside, same era.
Discovering, predicting, and planning with objects
J. D. Co-Reyes, R. Veerapaneni, M. Chang, M. Janner, C. Finn, J. Wu, J. Tenenbaum, and S. Levine · 2019
Cited alongside, same era.
AlignNet: Self-supervised alignment module, 2019
A. Creswell, L. Piloto, D. Barrett, K. Nikiforou, D. Raposo, M. Garnelo, P. Battaglia, and M. Shanahan · 2019
Cited alongside, same era.
Learning visual dynamics models of rigid objects using relational inductive biases
F. Ferreira, L. Shao, T. Asfour, and J. Bohg · 2019
Cited alongside, same era.
Multi-object representation learning with iterative variational inference
K. Greff, R. L. Kaufmann, R. Kabra, N. Watters, C. Burgess, D. Zoran, L. Matthey, M. Botvinick, and A. Lerchner · 2019
Cited alongside, same era.
Unsupervised learning of object keypoints for perception and control
T. Kulkarni, A. Gupta, C. Ionescu, S. Borgeaud, M. Reynolds, A. Zisserman, and V. Mnih · 2019
Cited alongside, same era.
Data association for multi-object tracking via deep neural networks
K. Yoon, D. Y. Kim, Y.-C. Yoon, and M. Jeon · 2019
Later among the works it cites.
Probing emergent semantics in predictive agents via question answering
A. Das, F. Carnevale, H. Merzic, L. Rimell, R. Schneider, J. Abramson, A. Hung, A. Ahuja, S. Clark, G. Wayne, et al · 2020
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Environmental drivers of systematicity and generalization in a situated agent
F. Hill, A. Lampinen, R. Schneider, S. Clark, M. Botvinick, J. L. McClelland, and A. Santoro · 2020
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Human instruction-following with deep reinforcement learning via transfer-learning from text
F. Hill, S. Mokra, N. Wong, and T. Harley · 2020
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Learning intuitive physics through objects, 2020
P. B. Luis S Piloto, Ari Weinstein and M. Botvinick · 2020
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Occlusion resistant learning of intuitive physics from videos, 2020
R. Riochet, J. Sivic, I. Laptev, and E. Dupoux · 2020
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Deep learning for person re-identification: A survey and outlook, 2020
M. Ye, J. Shen, G. Lin, T. Xiang, L. Shao, and S. C. H. Hoi · 2020
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A simple baseline for multi-object tracking
Y. Zhan, C. Wang, X. Wang, W. Zeng, and W. Liu · 2020
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