@conference {4700,
	title = {PHASE: PHysically-grounded Abstract Social Eventsfor Machine Social Perception},
	booktitle = {Shared Visual Representations in Human and Machine Intelligence (SVRHM) workshop at NeurIPS 2020},
	year = {2020},
	month = {12/2020},
	abstract = {<p>The ability to perceive and reason about social interactions in the context ofphysical environments is core to human social intelligence and human-machinecooperation. However, no prior dataset or benchmark has systematically evaluatedphysically grounded perception of complex social interactions that go beyondshort actions, such as high-fiving, or simple group activities, such as gathering.In this work, we create a dataset of physically-grounded abstract social events,PHASE, that resemble a wide range of real-life social interactions by includingsocial concepts such as helping another agent. PHASE consists of 2D animationsof pairs of agents moving in a continuous space generated procedurally using aphysics engine and a hierarchical planner. Agents have a limited field of view, andcan interact with multiple objects, in an environment that has multiple landmarksand obstacles. Using PHASE, we design a social recognition task and a social prediction task. PHASE is validated with human experiments demonstrating thathumans perceive rich interactions in the social events, and that the simulated agents behave similarly to humans. As a baseline model, we introduce a Bayesian inverse planning approach, SIMPLE (SIMulation, Planning and Local Estimation), which outperforms state-of-the-art feed-forward neural networks. We hope that PHASEcan serve as a difficult new challenge for developing new models that can recognize complex social interactions.</p>
},
	url = {https://openreview.net/forum?id=_bokm801zhx},
	author = {Aviv Netanyahu and Tianmin Shu and Boris Katz and Andrei Barbu and Joshua B. Tenenbaum}
}
