Bridging the Pose-Semantic Gap: A Cascade Framework for Text-Based Person Anomaly Search
For surveillance and anomaly detection, this work addresses the ambiguity between pose and semantics in text-based person search, offering a practical balance between efficiency and semantic reasoning.
Text-based person anomaly search suffers from a Pose-Semantic Gap where different actions share similar poses. The proposed SSDC framework decouples retrieval into coarse structural filtering and multi-agent semantic verification, achieving state-of-the-art performance on the PAB benchmark.
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-the-art performance by balancing efficiency and semantic reasoning.