Hallucination aware hypergraph memory for few shot RGB D anomaly detection

Cross-modal inconsistency is a major challenge for reliable RGB-D anomaly detection, especially under few-shot settings where normal multimodal patterns are insufficiently represented. In this work, we study hallucination in a restricted perception-level sense, referring to unreliable anomaly responses induced by inconsistent or insufficiently grounded multimodal evidence. Unlike textual hallucinations in LLMs/VLMs, the phenomenon considered here occurs in multimodal perception, where conflicting RGB appearance and depth geometry may lead to false defect alarms, missed anomalies, or inaccurate localization maps. To address this problem, we introduce Hallucination-Aware Hypergraph Memory (HA-HM), a framework that models high-order cross-modal consistency through a unified, learnable hypergraph. HA-HM integrates four synergistic components: a Learnable Hypergraph Constructor for adaptive multimodal grouping, a Consistency-Guided Memory Prototype Learner that stores high-fidelity normal prototypes, an Iterative Hypergraph Refiner for prototype-guided inconsistency correction, and a Hallucination Localization module for dense anomaly localization. Evaluations on the MVTec 3D-AD and Eyecandies RGB-D benchmarks demonstrate the effectiveness of HA-HM in few-shot multimodal anomaly detection and localization. Under challenging 1-, 2-, and 4-shot conditions, HA-HM achieves consistent improvements over existing training-based and training-free methods in image-level AUROC, pixel-level AUROC, and AUPRO. Ablation studies further validate the contribution of each component. These results suggest that explicitly modeling cross-modal consistency can improve the reliability of RGB-D anomaly detection systems.