UniMRE: Modality-Specialized Retrieval and Reasoning for Unified Multimodal Anomaly Detection

Unified multi-class anomaly detection has attracted growing interest as an efficient alternative to the conventional one-class-one-model paradigm, reducing training and maintenance costs for industrial deployment. However, real-world industrial defects span both appearance defects, such as scratches and contamination, and geometric defects, such as protrusions and deformations. Most existing unified methods rely primarily on RGB images and therefore lack explicit geometric cues for detecting structural anomalies. In this paper, we propose Unified Multimodal Retrieval-guided Experts (UniMRE), a unified framework that preserves category-specific normality and complementary appearance–geometry evidence through dual-stream retrieval-guided reasoning. UniMRE constructs class-conditional normal memories in separate RGB and joint RGBD feature spaces, where stream-wise retrieval provides stream-specific normal references and matching evidence for anomaly reasoning. Retrieval-guided expert pathways progressively reason over the stream-specific evidence, while lightweight bidirectional cross-stream conditioning enriches feature reasoning without altering the matching volumes. The resulting predictions are integrated by a learnable class-aware pixel fusion conditioned on local expert responses and nearest-normal retrieval cues. Extensive experiments on MVTec-3D, Eyecandies, and MulSen-AD demonstrate improved image-level anomaly detection over the existing unified multimodal baseline under the full-shot setting, together with strong few-shot performance and competitive results against specialized one-class-one-model approaches. Code is available at https://anonymous.4open.science/r/unimre-iclr-anon-62EA.