HAKE: A Knowledge Engine Foundation for Human Activity Understanding

Understand

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.