HAKE: A Knowledge Engine Foundation for Human Activity Understanding
نویسندگان
چکیده
Human activity understanding is of widespread interest in artificial intelligence and spans diverse applications like health care behavior analysis. Although there have been advances with deep learning, it remains challenging. The object recognition-like solutions usually try to map pixels semantics directly, but patterns are much different from patterns, thus hindering another success. In this work, we propose a novel paradigm reformulate task two-stage: first mapping an intermediate space spanned by atomic primitives, then programming detected primitives interpretable logic rules infer semantics. To afford representative primitive space, build knowledge base including 26+ M labels human priors or automatic discovering. Our framework, H xmlns:xlink="http://www.w3.org/1999/xlink">uman xmlns:xlink="http://www.w3.org/1999/xlink">A xmlns:xlink="http://www.w3.org/1999/xlink">ctivity xmlns:xlink="http://www.w3.org/1999/xlink">K xmlns:xlink="http://www.w3.org/1999/xlink">nowledge xmlns:xlink="http://www.w3.org/1999/xlink">E xmlns:xlink="http://www.w3.org/1999/xlink">ngine ( xmlns:xlink="http://www.w3.org/1999/xlink">HAKE ), exhibits superior generalization ability performance upon canonical methods on challenging benchmarks. Code data available at http://hake-mvig.cn/ .
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ژورنال
عنوان ژورنال: IEEE Transactions on Pattern Analysis and Machine Intelligence
سال: 2023
ISSN: ['1939-3539', '2160-9292', '0162-8828']
DOI: https://doi.org/10.1109/tpami.2022.3232797