Learning Perceptual Causality from Video

By Amy Sue Fire, Song-Chun Zhu. 2013

73 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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Citations in the corpus

Discovering Causal Signals in Images (2016, background): “Fire and Zhu [5, 6] use video data annotated with object status and actions to infer perceptual causality.”
CLEVRER: CoLlision Events for Video REpresentation and Reasoning (2019, neutral): “…et al., 2016; Fragkiadaki et al., 2016; Battaglia et al., 2016; Chang et al., 2017; Agrawal et al., 2016; Finn et al., 2016; Shao et al., 2014; Fire & Zhu, 2016; Pearl, 2009; Ye et al., 2018), either directly from images (Finn et al., 2016; Ebert et al., 2017; Watters et al., 2017; Lerer et…”
Dark, Beyond Deep: A Paradigm Shift to Cognitive AI with Humanlike Common Sense (2020, methodology): “As the fidelity and complexity of the simulation increased, Zhu et al. [233] were able to infer the forces impacting a seated human body, using a finite element method (FEM) to generate a mesh estimating the force on various body parts; Fig.”
Language to Action: Towards Interactive Task Learning with Physical Agents (2018, background): “Recent work has investigated learning physics of the world from videos [ Fire and Zhu, 2016 ] and simulations [ Wu et al. , 2017 ] .”
ThreeDWorld: A Platform for Interactive Multi-Modal Physical Simulation (2020, background): “Differentiable forward predictors that mimic human-level intuitive physical understanding have emerged as being of importance for enabling deep-learning based approaches to model-based planning and control applications [25, 4, 29, 15, 5, 9, 2, 36, 13, 14, 32, 46].”
Learning a Generative Model for Multi‐Step Human‐Object Interactions from Videos (2019, methodology): “Although this literature is broad, it is largely based on fully supervised classiﬁcation of video clips [LMSR08,EBMM03,RA09] and only a few methods exist to synthesize interactions [SPYZ11,PJZ11] or to structure the causal dependencies of human actions [FZ16].”
Grounding Physical Concepts of Objects and Events Through Dynamic Visual Reasoning (2021, background): “Much research has been studying dynamic modeling for physical scenes (Lerer et al., 2016; Battaglia et al., 2013; Mottaghi et al., 2016; Finn et al., 2016; Shao et al., 2014; Fire & Zhu, 2015; Ye et al., 2018; Li et al., 2019b).”