Reviews: Modeling Expectation Violation in Intuitive Physics with Coarse Probabilistic Object Representations

Many studies have looked at the ideas of physics simulation as a cognitive model. In such works, physics engines are usually employed as a model of human cognition of physical tasks, with the perception part of the task is often abstracted away. In parallel, data driven model have been frequently used to learn to parse raw visual inputs to detect or locate objects, frequently without using any explicit model of the physical world. This paper tries to bridge these two fields to build a complete model of how humans perceive certain physical scenarios, from raw pixels to expectations over objects. Whereas all of the parts employed in the proposed "pipeline" are based on previous works, their arrangement into this contiguous framework is new, as is the human and modeled results on the new dataset the authors also present.