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

• URL: http://arxiv.org/abs/2604.23282v1
• Date: Sat, 25 Apr 2026 12:53:15 GMT
• Title: Bridging the Pose-Semantic Gap: A Cascade Framework for Text-Based Person Anomaly Search
• Abstract summary: Text-based person anomaly search retrieves specific behavioral events from surveillance archives using natural-language queries.<n>Recent pose-aware methods face a fundamental Pose-Semantic Gap: semantically different actions can share similar skeletal geometries.<n>We propose the Structure-Semantic Decoupled Cascade (SSDC) framework, which decouples retrieval into two stages.
• Score: 45.34874989015716
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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