HAKE: A Knowledge Engine Foundation for Human Activity Understanding

• URL: http://arxiv.org/abs/2202.06851v2
• Date: Fri, 15 Sep 2023 08:00:19 GMT
• Title: HAKE: A Knowledge Engine Foundation for Human Activity Understanding
• Authors: Yong-Lu Li, Xinpeng Liu, Xiaoqian Wu, Yizhuo Li, Zuoyu Qiu, Liang Xu, Yue Xu, Hao-Shu Fang, Cewu Lu
• Abstract summary: Human activity understanding is of widespread interest in artificial intelligence and spans diverse applications like health care and behavior analysis. We propose a novel paradigm to reformulate this task in two stages: first mapping pixels to an intermediate space spanned by atomic activity primitives, then programming detected primitives with interpretable logic rules to infer semantics. Our framework, the Human Activity Knowledge Engine (HAKE), exhibits superior generalization ability and performance upon challenging benchmarks.
• Score: 65.24064718649046
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 in 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 success. In this work, we propose a novel paradigm to reformulate this task in two stages: 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, the Human Activity Knowledge Engine (HAKE), exhibits superior generalization ability and performance upon canonical methods on challenging benchmarks. Code and data are available at http://hake-mvig.cn/.

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