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While current vision algorithms excel at many challenging tasks, it is unclear how well they understand the physical dynamics of real-world environments.
Understanding projectile acceleration
H. Hecht and M. Bertamini · 1939
Earlier work this paper cites.
Long-term memory for a common object
R. S. Nickerson and M. J. Adams · 1979
Earlier work this paper cites.
Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography
M. A. Fischler and R. C. Bolles · 1981
Earlier work this paper cites.
Object permanence in five-month-old infants
R. Baillargeon, E. S. Spelke, and S. Wasserman · 1985
Earlier work this paper cites.
Principles of object perception
E. S. Spelke · 1990
Earlier work this paper cites.
Infants’ knowledge of objects: Beyond object files and object tracking
S. Carey and F. Xu · 2001
Earlier work this paper cites.
Change Blindness: Theory and Consequences
D. J. Simons and M. S. Ambinder · 2005
Earlier work this paper cites.
How do infants reason about physical events?
R. Baillargeon, J. Li, Y. Gertner, and D. Wu · 2011
Earlier work this paper cites.
Simulation as an engine of physical scene understanding
P. W. Battaglia, J. B. Hamrick, and J. B. Tenenbaum · 2013
Earlier work this paper cites.
Reconciling intuitive physics and Newtonian mechanics for colliding objects
A. N. Sanborn, V. K. Mansinghka, and T. L. Griffiths · 2013
Earlier work this paper cites.
Sources of Uncertainty in Intuitive Physics
K. A. Smith and E. Vul · 2013
Earlier work this paper cites.
Humans predict liquid dynamics using probabilistic simulation
C. J. Bates, P. W. Battaglia, I. Yildirim, and J. B. Tenenbaum · 2015
Earlier work this paper cites.
Fast r-cnn
R. Girshick · 2015
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2015
Earlier work this paper cites.
Learning to poke by poking: Experiential learning of intuitive physics
P. Agrawal, A. V. Nair, P. Abbeel, J. Malik, and S. Levine · 2016
Earlier work this paper cites.
Interaction networks for learning about objects, relations and physics
P. Battaglia, R. Pascanu, M. Lai, D. J. Rezende, et al · 2016
Earlier work this paper cites.
Unsupervised learning for physical interaction through video prediction
C. Finn, I. Goodfellow, and S. Levine · 2016
Earlier work this paper cites.
Learning visual predictive models of physics for playing billiards
K. Fragkiadaki, P. Agrawal, S. Levine, and J. Malik · 2016
Earlier work this paper cites.
Learning physical intuition of block towers by example
A. Lerer, S. Gross, and R. Fergus · 2016
Earlier work this paper cites.
To fall or not to fall: A visual approach to physical stability prediction
W. Li, S. Azimi, A. Leonardis, and M. Fritz · 2016
Earlier work this paper cites.
Anticipating visual representations from unlabeled video
C. Vondrick, H. Pirsiavash, and A. Torralba · 2016
Earlier work this paper cites.
A comparative evaluation of approximate probabilistic simulation and deep neural networks as accounts of human physical scene understanding
R. Zhang, J. Wu, C. Zhang, W. T. Freeman, and J. B. Tenenbaum · 2016
Earlier work this paper cites.
A compositional object-based approach to learning physical dynamics
M. B. Chang, T. Ullman, A. Torralba, and J. B. Tenenbaum · 2017
Earlier work this paper cites.
Mind Games: Game Engines as an Architecture for Intuitive Physics
T. D. Ullman, E. Spelke, P. Battaglia, and J. B. Tenenbaum · 2017
Cited alongside, same era.
Relational inductive biases, deep learning, and graph networks
P. W. Battaglia, J. B. Hamrick, V. Bapst, A. Sanchez-Gonzalez, V. Zambaldi, M. Malinowski, A. Tacchetti, D. Raposo, A. Santoro, R. Faulkner, et al · 2018
Cited alongside, same era.
Stochastic video generation with a learned prior
E. Denton and R. Fergus · 2018
Cited alongside, same era.
Robustness via retrying: Closed-loop robotic manipulation with self-supervised learning
F. Ebert, S. Dasari, A. X. Lee, S. Levine, and C. Finn · 2018
Cited alongside, same era.
T. Gebru, J. Morgenstern, B. Vecchione, J. W. Vaughan, H. Wallach, H. Daumé III, and K. Crawford · 2018
Cited alongside, same era.
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.
Relational forward models for multi-agent learning
A. Tacchetti, H. F. Song, P. A. M. Mediano, V. F. Zambaldi, J. Kramár, N. C. Rabinowitz, T. Graepel, M. Botvinick, and P. W. Battaglia · 2019
Later among the works it cites.
High fidelity video prediction with large stochastic recurrent neural networks
R. Villegas, A. Pathak, H. Kannan, D. Erhan, Q. V. Le, and H. Lee · 2019
Later among the works it cites.
N. Watters, L. Matthey, M. Bosnjak, C. P. Burgess, and A. Lerchner · 2019
Later among the works it cites.
Compositional video prediction
Y. Ye, M. Singh, A. Gupta, and S. Tulsiani · 2019
Later among the works it cites.
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O. Groth, F. B. Fuchs, I. Posner, and A. Vedaldi · 2018
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Learning to play with intrinsically-motivated self-aware agents
N. Haber, D. Mrowca, L. Fei-Fei, and D. L. Yamins · 2018
Cited alongside, same era.
Stochastic adversarial video prediction
A. X. Lee, R. Zhang, F. Ebert, P. Abbeel, C. Finn, and S. Levine · 2018
Cited alongside, same era.
Roboturk: A crowdsourcing platform for robotic skill learning through imitation
A. Mandlekar, Y. Zhu, A. Garg, J. Booher, M. Spero, A. Tung, J. Gao, J. Emmons, A. Gupta, E. Orbay, et al · 2018
Cited alongside, same era.
Flexible neural representation for physics prediction
D. Mrowca, C. Zhuang, E. Wang, N. Haber, L. F. Fei-Fei, J. Tenenbaum, and D. L. Yamins · 2018
Cited alongside, same era.
Probing physics knowledge using tools from developmental psychology
L. Piloto, A. Weinstein, A. Ahuja, M. Mirza, G. Wayne, D. Amos, C.-c. Hung, and M. Botvinick · 2018
Cited alongside, same era.
Large-scale, high-resolution comparison of the core visual object recognition behavior of humans, monkeys, and state-of-the-art deep artificial neural networks
R. Rajalingham, E. B. Issa, P. Bashivan, K. Kar, K. Schmidt, and J. J. DiCarlo · 2018
Cited alongside, same era.
Rapid trial-and-error learning with simulation supports flexible tool use and physical reasoning
K. R. Allen, K. A. Smith, and J. B. Tenenbaum · 2020
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Learning physical graph representations from visual scenes
D. M. Bear, C. Fan, D. Mrowca, Y. Li, S. Alter, A. Nayebi, J. Schwartz, L. Fei-Fei, J. Wu, J. B. Tenenbaum, et al · 2020
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D. Ding, F. Hill, A. Santoro, and M. Botvinick · 2020
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ThreeDWorld: A Platform for Interactive Multi-Modal Physical Simulation
C. Gan, J. Schwartz, S. Alter, M. Schrimpf, J. Traer, J. De Freitas, J. Kubilius, A. Bhandwaldar, N. Haber, M. Sano, K. Kim, E. Wang, D. Mrowca, M. Lingelbach, A. Curtis, K. Feigelis, D. M. Bear, D. Gutfreund, D. Cox, J. J. DiCarlo, J. McDermott, J. B. Tenenbaum, and D. L. K. Yamins · 2020
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Shortcut learning in deep neural networks
R. Geirhos, J.-H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F. A. Wichmann · 2020
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Forward prediction for physical reasoning
R. Girdhar, L. Gustafson, A. Adcock, and L. van der Maaten · 2020
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Contrastive learning of structured world models
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Visual grounding of learned physical models
Y. Li, T. Lin, K. Yi, D. Bear, D. Yamins, J. Wu, J. Tenenbaum, and A. Torralba · 2020
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Goal-aware prediction: Learning to model what matters
S. Nair, S. Savarese, and C. Finn · 2020
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Occlusion resistant learning of intuitive physics from videos
R. Riochet, J. Sivic, I. Laptev, and E. Dupoux · 2020
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Learning to simulate complex physics with graph networks
A. Sanchez-Gonzalez, J. Godwin, T. Pfaff, R. Ying, J. Leskovec, and P. Battaglia · 2020
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Learning predictive models from observation and interaction
K. Schmeckpeper, A. Xie, O. Rybkin, S. Tian, K. Daniilidis, S. Levine, and C. Finn · 2020
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Training data-efficient image transformers & distillation through attention
H. Touvron, M. Cord, M. Douze, F. Massa, A. Sablayrolles, and H. Jégou · 2020
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Entity abstraction in visual model-based reinforcement learning
R. Veerapaneni, J. D. Co-Reyes, M. Chang, M. Janner, C. Finn, J. Wu, J. Tenenbaum, and S. Levine · 2020
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Perception of soft materials relies on physics-based object representations: Behavioral and computational evidence
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