GradCO: Behavior-Aware Gradient-Conflict Offloading for Text-Based Person Anomaly Search

GradCO is a new method for Text-based Person Anomaly Search (TPAS) that addresses two key challenges: gradient conflict from same-identity, different-behavior hard negatives and the dominance of strong appearance cues over weak behavioral evidence. It introduces a Behavior-Aware Mixture-of-Experts (B-MoE) to separate appearance, normal-behavior, and anomalous-behavior streams, Conflict Offload Learning (COL) to reduce conflicting gradients via pair-conditioned anchors, and Hierarchical Intent Alignment (HIA) to aggregate expert similarities with query-conditioned weights. Experiments on five TPAS/TIPR benchmarks demonstrate that GradCO significantly outperforms state-of-the-art methods, validating its design choices.

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