Compositional Benchmark Synthesis for Hierarchical Human Action Recognition

• URL: http://arxiv.org/abs/2608.10765v1
• Date: Tue, 11 Aug 2026 10:22:22 GMT
• Title: Compositional Benchmark Synthesis for Hierarchical Human Action Recognition
• Abstract summary: A benchmark-generation and evaluation frameworkis proposed that synthesizes a four-level hierarchical-intention benchmark.<n> Episodes are assembled by a transition model under a subject-consistency constraint.<n>A coverage-aware sampler reduces the subject usage Gini from 0.566 to 0.248.
• Score: 4.812022454180755
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 intentions (LLIs), and high-level intentions (HLIs), from a flat single-label action corpus while retaining real pre-extracted features at the action level. Episodes are assembled by a transition model under a subject-consistency constraint, and a coverage-aware sampler reduces the subject usage Gini from 0.566 to 0.248. Synthesizing such a benchmark raises a circular-supervision risk that recorded datasets avoid: if the rules generating the episodes also govern the evaluation, models can succeed by recovering the generator rather than through genuine reasoning. Validity is addressed by design, holding sequence-generation rules disjoint from the first-order-logic rules used at evaluation. The instantiation yields 15,002 episodes. Four reference baselines from different model families characterize difficulty, not as recognition methods. A compositional held-out gap of 0.13 to 0.17 macro-F1 appears across all baselines, including a graph-aware model that recognizes best yet does not close the gap, indicating a structural property of the benchmark rather than a model artifact. A logic-free baseline still violates the held-out semantic rules above their intrinsic data rate, and the order-destroying control changes macro-F1 within seed variation, serving as a generator-consistency check. Theontology, transition model, and generator are released so the benchmark can beregenerated and extended.

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