PIP: Physical Interaction Prediction via Mental Simulation with Span Selection
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Soujanya Poria | Bihan Wen | Cheston Tan | Jiafei Duan | Samson Yu | Cheston Tan | B. Wen | Soujanya Poria | Jiafei Duan | Samson Yu
[1] James R. Kubricht,et al. Intuitive Physics: Current Research and Controversies , 2017, Trends in Cognitive Sciences.
[2] Jiajun Wu,et al. Physics 101: Learning Physical Object Properties from Unlabeled Videos , 2016, BMVC.
[3] J. Tenenbaum,et al. Mind Games: Game Engines as an Architecture for Intuitive Physics , 2017, Trends in Cognitive Sciences.
[4] Greg Mori,et al. COPHY: Counterfactual Learning of Physical Dynamics , 2019, ICLR.
[5] M. Bethge,et al. Shortcut learning in deep neural networks , 2020, Nature Machine Intelligence.
[6] R'emi Louf,et al. HuggingFace's Transformers: State-of-the-art Natural Language Processing , 2019, ArXiv.
[7] John G. Mikhael,et al. Functional neuroanatomy of intuitive physical inference , 2016, Proceedings of the National Academy of Sciences.
[8] Jessica B. Hamrick,et al. Simulation as an engine of physical scene understanding , 2013, Proceedings of the National Academy of Sciences.
[9] Thomas L. Griffiths,et al. Think again? The amount of mental simulation tracks uncertainty in the outcome , 2015, CogSci.
[10] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[11] Pieter Abbeel,et al. VideoGPT: Video Generation using VQ-VAE and Transformers , 2021, ArXiv.
[12] Jitendra Malik,et al. Which Tasks Should Be Learned Together in Multi-task Learning? , 2019, ICML.
[13] Jitendra Malik,et al. Learning Visual Predictive Models of Physics for Playing Billiards , 2015, ICLR.
[14] Natalia Gimelshein,et al. PyTorch: An Imperative Style, High-Performance Deep Learning Library , 2019, NeurIPS.
[15] Yutaka Satoh,et al. Would Mega-scale Datasets Further Enhance Spatiotemporal 3D CNNs? , 2020, ArXiv.
[16] B. Scholl,et al. Seeing stability: Intuitive physics automatically guides selective attention , 2016 .
[17] Ronald M. Summers,et al. Spatial-Temporal Convolutional LSTMs for Tumor Growth Prediction by Learning 4D Longitudinal Patient Data , 2019, ArXiv.
[18] 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).
[19] Liang Zhang,et al. Self-Supervised Learning to Detect Key Frames in Videos , 2020, Sensors.
[20] Neil R. Bramley,et al. Intuitive experimentation in the physical world , 2018, Cognitive Psychology.
[21] Nancy Kanwisher,et al. Physion: Evaluating Physical Prediction from Vision in Humans and Machines , 2021, ArXiv.
[22] Jean Ponce,et al. Computer Vision: A Modern Approach , 2002 .
[23] Zhihui Lin,et al. CMS-LSTM: Context-Embedding and Multi-Scale Spatiotemporal-Expression LSTM for Video Prediction , 2021, ArXiv.
[24] Marcelo H. Ang,et al. AVoE: A Synthetic 3D Dataset on Understanding Violation of Expectation for Artificial Cognition , 2021, ArXiv.
[25] Soumith Chintala,et al. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks , 2015, ICLR.
[26] R. Fleming. Visual perception of materials and their properties , 2014, Vision Research.
[27] Mario Fritz,et al. Visual stability prediction for robotic manipulation , 2017, 2017 IEEE International Conference on Robotics and Automation (ICRA).
[28] Geoffrey E. Hinton,et al. Deep learning for AI , 2021, Commun. ACM.
[29] Jianhua Lin,et al. Divergence measures based on the Shannon entropy , 1991, IEEE Trans. Inf. Theory.
[30] 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).
[31] Claudio de’Sperati,et al. Speed Biases With Real-Life Video Clips , 2018, Front. Integr. Neurosci..
[32] Abhinav Gupta,et al. Interpretable Intuitive Physics Model , 2018, ECCV.
[33] Cheston Tan,et al. A Survey of Embodied AI: From Simulators to Research Tasks , 2021, IEEE Transactions on Emerging Topics in Computational Intelligence.
[34] Lawrence Carin,et al. SpanPredict: Extraction of Predictive Document Spans with Neural Attention , 2021, NAACL.
[35] Kevin A. Smith,et al. Sources of uncertainty in intuitive physics , 2012, CogSci.
[36] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[37] Mario Fritz,et al. To Fall Or Not To Fall: A Visual Approach to Physical Stability Prediction , 2016, ArXiv.
[38] Razvan Pascanu,et al. Interaction Networks for Learning about Objects, Relations and Physics , 2016, NIPS.
[39] Rob Fergus,et al. Learning Physical Intuition of Block Towers by Example , 2016, ICML.
[40] Katsushi Ikeuchi,et al. Scene Understanding by Reasoning Stability and Safety , 2015, International Journal of Computer Vision.
[41] Neil R. Bramley,et al. Limits on simulation approaches in intuitive physics , 2021, Cognitive Psychology.
[42] Ronald J. Williams,et al. A Learning Algorithm for Continually Running Fully Recurrent Neural Networks , 1989, Neural Computation.
[43] 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.
[44] George Kurian,et al. Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation , 2016, ArXiv.
[45] Ming-Wei Chang,et al. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding , 2019, NAACL.
[46] Jason Fischer,et al. When it all falls down: the relationship between intuitive physics and spatial cognition , 2020, Cognitive research: principles and implications.
[47] J. Tenenbaum,et al. Intuitive Theories , 2020, Encyclopedia of Creativity, Invention, Innovation and Entrepreneurship.
[48] David J. Fleet,et al. Estimating contact dynamics , 2009, 2009 IEEE 12th International Conference on Computer Vision.
[49] Clément Gosselin,et al. Safe, Stable and Intuitive Control for Physical Human-Robot Interaction , 2009, 2009 IEEE International Conference on Robotics and Automation.
[50] Jakob Uszkoreit,et al. Scaling Autoregressive Video Models , 2019, ICLR.
[51] Andrea Vedaldi,et al. ShapeStacks: Learning Vision-Based Physical Intuition for Generalised Object Stacking , 2018, ECCV.
[52] Sergey Levine,et al. Unsupervised Learning for Physical Interaction through Video Prediction , 2016, NIPS.
[53] Michael R. Waldmann,et al. The Oxford handbook of causal reasoning , 2017 .
[54] Dit-Yan Yeung,et al. Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting , 2015, NIPS.
[55] Jason Fischer,et al. A striking take on mass inferences from collisions , 2021, Journal of Vision.