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[1] Andrea Vedaldi,et al. ShapeStacks: Learning Vision-Based Physical Intuition for Generalised Object Stacking , 2018, ECCV.
[2] S. Kosslyn,et al. Topographical representations of mental images in primary visual cortex , 1995, Nature.
[3] Li Fei-Fei,et al. ImageNet: A large-scale hierarchical image database , 2009, CVPR.
[4] Mauro Da Lio,et al. On the Road with 16 Neurons: Mental Imagery with Bio-inspired Deep Neural Networks , 2020, ArXiv.
[5] Cheston Tan,et al. A Survey of Embodied AI: From Simulators to Research Tasks , 2021, IEEE Transactions on Emerging Topics in Computational Intelligence.
[6] James R. Kubricht,et al. Intuitive Physics: Current Research and Controversies , 2017, Trends in Cognitive Sciences.
[7] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[8] Daniel L. Schwartz,et al. Analog Imagery in Mental Model Reasoning: Depictive Models , 1996, Cognitive Psychology.
[9] Chuang Gan,et al. CLEVRER: CoLlision Events for Video REpresentation and Reasoning , 2020, ICLR.
[10] Guigang Zhang,et al. Deep Learning , 2016, Int. J. Semantic Comput..
[11] N. Hari Narayanan,et al. Diagrammatic Reasoning: Cognitive and Computational Perspectives , 1995 .
[12] M. Bethge,et al. Shortcut learning in deep neural networks , 2020, Nature Machine Intelligence.
[13] Rohit Girdhar,et al. Forward Prediction for Physical Reasoning , 2020, ArXiv.
[14] Mario Fritz,et al. To Fall Or Not To Fall: A Visual Approach to Physical Stability Prediction , 2016, ArXiv.
[15] Joshua B. Tenenbaum,et al. A Compositional Object-Based Approach to Learning Physical Dynamics , 2016, ICLR.
[16] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[17] Jianhua Lin,et al. Divergence measures based on the Shannon entropy , 1991, IEEE Trans. Inf. Theory.
[18] Nancy Kanwisher,et al. Physion: Evaluating Physical Prediction from Vision in Humans and Machines , 2021, ArXiv.
[19] Nico Bruns,et al. Blender , 2020, Der Unfallchirurg.
[20] M. Kunda. Visual mental imagery: A view from artificial intelligence , 2018, Cortex.
[21] John G. Mikhael,et al. Functional neuroanatomy of intuitive physical inference , 2016, Proceedings of the National Academy of Sciences.
[22] Yutaka Satoh,et al. Would Mega-scale Datasets Further Enhance Spatiotemporal 3D CNNs? , 2020, ArXiv.
[23] S. Kosslyn,et al. The role of area 17 in visual imagery: convergent evidence from PET and rTMS. , 1999, Science.
[24] Jitendra Malik,et al. Learning Visual Predictive Models of Physics for Playing Billiards , 2015, ICLR.
[25] Ming-Wei Chang,et al. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding , 2019, NAACL.
[26] Lawrence Carin,et al. SpanPredict: Extraction of Predictive Document Spans with Neural Attention , 2021, NAACL.
[27] Chuang Gan,et al. ThreeDWorld: A Platform for Interactive Multi-Modal Physical Simulation , 2020, ArXiv.
[28] Yutaka Satoh,et al. Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet? , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[29] Sergey Levine,et al. Unsupervised Learning for Physical Interaction through Video Prediction , 2016, NIPS.
[30] Ronald J. Williams,et al. A Learning Algorithm for Continually Running Fully Recurrent Neural Networks , 1989, Neural Computation.
[31] Ali Farhadi,et al. "What Happens If..." Learning to Predict the Effect of Forces in Images , 2016, ECCV.
[32] Rob Fergus,et al. Learning Physical Intuition of Block Towers by Example , 2016, ICML.
[33] Angelo Cangelosi,et al. A Neural Network model for spatial mental imagery investigation: A study with the humanoid robot platform iCub , 2011, The 2011 International Joint Conference on Neural Networks.
[34] David J. Fleet,et al. Estimating contact dynamics , 2009, 2009 IEEE 12th International Conference on Computer Vision.
[35] Brian V. Funt,et al. Problem-Solving with Diagrammatic Representations , 1980, Artif. Intell..
[36] Natalia Gimelshein,et al. PyTorch: An Imperative Style, High-Performance Deep Learning Library , 2019, NeurIPS.
[37] Soumith Chintala,et al. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks , 2015, ICLR.
[38] Felix Hill,et al. Object-based attention for spatio-temporal reasoning: Outperforming neuro-symbolic models with flexible distributed architectures , 2020, ArXiv.
[39] Thomas Wolf,et al. HuggingFace's Transformers: State-of-the-art Natural Language Processing , 2019, ArXiv.
[40] Bernard Meltzer,et al. Analogical Representations of Naive Physics , 1989, Artif. Intell..
[41] Christian Wolf,et al. COPHY: Counterfactual Learning of Physical Dynamics , 2020, ICLR.
[42] Cheston Tan,et al. SPACE: A Simulator for Physical Interactions and Causal Learning in 3D Environments , 2021, 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW).
[43] D. Proffitt,et al. Heuristic judgment of mass ratio in two-body collisions , 1994, Perception & psychophysics.
[44] H. Furth. Object permanence in five-month-old infants. , 1987, Cognition.
[45] Dit-Yan Yeung,et al. Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting , 2015, NIPS.
[46] Nicolas Thome,et al. Disentangling Physical Dynamics From Unknown Factors for Unsupervised Video Prediction , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[47] Pascal Vincent,et al. Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion , 2010, J. Mach. Learn. Res..
[48] Razvan Pascanu,et al. Interaction Networks for Learning about Objects, Relations and Physics , 2016, NIPS.
[49] R. Baillargeon,et al. How Do Infants Reason about Physical Events , 2010 .
[50] Deva Ramanan,et al. CATER: A diagnostic dataset for Compositional Actions and TEmporal Reasoning , 2020, ICLR.
[51] Jessica B. Hamrick,et al. Simulation as an engine of physical scene understanding , 2013, Proceedings of the National Academy of Sciences.
[52] George Kurian,et al. Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation , 2016, ArXiv.
[53] Jiajun Wu,et al. Entity Abstraction in Visual Model-Based Reinforcement Learning , 2019, CoRL.