Cross-Modal Distillation in Industrial Anomaly Detection: Exploring Efficient Multi-Modal IAD

Recent studies of multi-modal Industrial Anomaly Detection (IAD) based on point clouds and RGB images indicated the importance of exploiting redundancy and complementarity among modalities for accurate classification and segmentation. However, achieving multi-modal IAD in practical production lines remains a work in progress that requires consideration of the trade-offs between costs and benefits associated with introducing new modalities, while ensuring compatibility with current processes. Combining fast in-line inspections with high-resolution, time-consuming, near-line characterization techniques to enhance detection accuracy fits well into the existing quality control process, but only part of the samples can be tested with expensive near-line methods. Thus, the model must have the ability to leverage multi-modal training and handle incomplete modalities during inference. One solution is generating cross-modal hallucination to transfer knowledge among modalities for missing modality issues. In this paper, we propose CMDIAD, a Cross-Modal Distillation framework for IAD to demonstrate the feasibility of Multi-modal Training, Few-modal Inference pipeline. Moreover, we investigate reasons behind the asymmetric performance improvement using point clouds or RGB images as main modality of inference. This lays the foundation of our future multi-modal dataset construction for efficient IAD from manufacturing scenarios.

Join, select, and insert: efficient out-of-core algorithms for hierarchical segmentation trees

Binary Partition Hierarchies (BPH) and minimum spanning trees are fundamental data structures involved in hierarchical analysis such as quasi-flat zones or watershed. However, classical BPH construction algorithms require to have the whole data in memory, which prevent the processing of large images that cannot fit entirely in the main memory of the computer. To cope with this problem, an algebraic framework leading to a high level calculus was introduced allowing an out-of-core computation of BPHs. This calculus relies on three operations: select, join, and insert. In this article, we introduce three efficient algorithms to perform these operations providing pseudo-code and complexity analysis.