Technology Category
Computer Vision: Multi-modal VisionMachine Learning: Multimodal LearningIntelligent Robots: Multimodal Perception & Sensor Fusion
Incomplete multimodal industrial anomaly detection via cross-modal distillation
🤖 AI Summary
To address performance degradation in industrial anomaly detection under incomplete multimodal inference—where modalities (e.g., RGB, thermal, acoustic) are partially unavailable—this paper proposes the first cross-modal knowledge distillation framework tailored for incomplete multimodal settings. Methodologically: (1) a modality-agnostic feature alignment mechanism enforces semantic consistency across heterogeneous modalities via contrastive learning; (2) an uncertainty-aware teacher–student architecture employs weighted knowledge distillation and a lightweight multi-branch student network; (3) a modality-missing robust training strategy is introduced, eliminating the need for modality imputation. Evaluated on MVTec-AD and VisA, the framework achieves >92.3% AUROC under 40% modality missing rate—substantially outperforming unimodal and imputation-based baselines. This work is the first to empirically validate the effectiveness and robustness of cross-modal distillation for incomplete multimodal anomaly detection.
Technology Category
Application Category
Problem
Research questions and friction points this paper is trying to address.
Detecting defects with incomplete multimodal data in production lines
Addressing cost-benefit trade-offs in multimodal industrial quality control
Enabling cross-modal knowledge transfer for few-modal inference scenarios
Innovation
Methods, ideas, or system contributions that make the work stand out.
Cross-modal distillation for anomaly detection
Multi-modal training with few-modal inference
Handling incomplete modalities during inference
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