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Human activity understanding is of widespread interest in artificialintelligence and spans diverse applications like health care and behavioranalysis. Although there have been advances in deep learning, it remainschallenging. The object recognition-like solutions usually try to map pixels tosemantics directly, but activity patterns are much different from objectpatterns, thus hindering success. In this work, we propose a novel paradigm toreformulate this task in two stages: first mapping pixels to an intermediatespace spanned by atomic activity primitives, then programming detectedprimitives with interpretable logic rules to infer semantics. To afford arepresentative primitive space, we build a knowledge base including 26+ Mprimitive labels and logic rules from human priors or automatic discovering.Our framework, the Human Activity Knowledge Engine (HAKE), exhibits superiorgeneralization ability and performance upon canonical methods on challengingbenchmarks. Code and data are available at http://hake-mvig.cn/.

HAKE: A Knowledge Engine Foundation for Human Activity Understanding