FIND: Few-Shot Anomaly Inspection with Normal-Only Multi-Modal Data

Abstract

Multi-modal anomaly detection (MAD) improves industrial inspection by exploiting complementary 2D and 3D data. However, existing methods struggle in few-shot scenarios due to limited data and modality gaps. Current approaches either fuse multi-modal features or align cross-modal representations; however, they often suffer from high false-positive rates and fail to detect subtle defects, especially when training samples are scarce. To address these challenges, we propose the first few-shot MAD method FIND, a novel dual-student framework that integrates intra-modal reverse distillation and cross-modal feature mapping. FIND employs modality-specific teachers and two collaborative students: an intra-modal student for fine-grained anomaly localization via reverse distillation, and a cross-modal student that captures inter-modal correspondences to detect cross-modal inconsistencies. Extensive experiments on MVTec-3D-AD and Eyecandies show that FIND significantly outperforms state-of-the-art methods in both full-shot and few-shot settings. Ablation studies validate the complementary roles of intra- and cross-modal distillation. Our work significantly advances MAD robustness in data-scarce industrial applications.