Bridging the Pose-Semantic Gap: A Cascade Framework for Text-Based Person Anomaly Search

Text-based person anomaly search retrieves specific behavioral events from surveillance archives using natural-language queries.Although recent pose-aware methods align geometric structures well, they face a fundamental Pose-Semantic Gap: semantically different actions can share similar skeletal geometries.While Multimodal Large Language Models (MLLMs) can reduce this ambiguity, using them for large-scale retrieval is computationally prohibitive.We propose the Structure-Semantic Decoupled Cascade (SSDC) framework, which decouples retrieval into two stages:(1) Structure-Aware Coarse Retrieval, where a lightweight model quickly filters candidates by skeletal similarity; and (2) Detective Squad Interaction, a multi-agent semantic verification module.The squad consists of a Detective for fast binary filtering, an Analyst for evidence extraction, and a Writer for semantic synthesis.Finally, we re-rank candidates by fusing the synthesized captions with structural priors.Experiments on the PAB benchmark show that SSDC achieves state-of-theart performance by balancing efficiency and semantic reasoning.Our code is available