Human activity understanding is of widespread interest in artificial
intelligence and spans diverse applications like health care and behavior
analysis. Although there have been advances with deep learning, it remains
challenging. The object recognition-like solutions usually try to map pixels to
semantics directly, but activity patterns are much different from object
patterns, thus hindering another success. In this work, we propose a novel
paradigm to reformulate this task in two-stage: first mapping pixels to an
intermediate space spanned by atomic activity primitives, then programming
detected primitives with interpretable logic rules to infer semantics. To
afford a representative primitive space, we build a knowledge base including
26+ M primitive labels and logic rules from human priors or automatic
discovering. Our framework, Human Activity Knowledge Engine (HAKE), exhibits
superior generalization ability and performance upon canonical methods on
challenging benchmarks. Code and data are available at this http URL.

该论文提出了一种新的方法来解决人类活动理解中的图像识别问题，该方法包括将像素映射到原子活动原始空间内，然后利用可解释的逻辑规则进行语义推理。该方法获得了很大的改进和成功，并且在当前研究领域有着广泛的应用。