Compositional Benchmark Synthesis for Hierarchical Human Action Recognition

Recognizing human behavior across levels of abstraction, from atomic actions to long-horizon intentions, requires data annotated along a semantic hierarchy. Large corpora provide isolated, atomically labeled clips without temporal composition, whereas recorded composite-activity corpora offer shallow, domain-narrow, fixedhierarchies. A benchmark-generation and evaluation frameworkis proposed that synthesizes a four-level hierarchical-intention benchmark, spanning actions, activities, low-level i

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ethics.ai (11 August 2026), “Compositional Benchmark Synthesis for Hierarchical Human Action Recognition,” evidence record 18413, https://ethics.ai/record/18413 (originally published by arXiv).

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