mlsanityData to 7 Oct 2026

Multimodal Industrial Anomaly Detection by Crossmodal Feature Mapping

arXiv 2312.04521 ↗First posted 7 Dec 2023cs.CVcited by 41

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Abstract (arXiv)

The paper explores the industrial multimodal Anomaly Detection (AD) task, which exploits point clouds and RGB images to localize anomalies. We introduce a novel light and fast framework that learns to map features from one modality to the other on nominal samples. At test time, anomalies are detected by pinpointing inconsistencies between observed and mapped features. Extensive experiments show that our approach achieves state-of-the-art detection and segmentation performance in both the standard and few-shot settings on the MVTec 3D-AD dataset while achieving faster inference and occupying less memory than previous multimodal AD methods. Moreover, we propose a layer-pruning technique to improve memory and time efficiency with a marginal sacrifice in performance.

Key facts. Cited by 41 papers in the map (data through 7 Oct 2026). Builds on 40 of its 52 references that are in the map. First posted on arXiv 7 Dec 2023.

Builds on

40 of 52 references.

References · 40

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  2. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  3. 2023Segment Anythingcited by 8,158
  4. 2021Masked Autoencoders Are Scalable Vision Learnerscited by 7,339
  5. 2021Emerging Properties in Self-Supervised Vision Transformerscited by 6,893
  6. 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,384
  7. 2015ShapeNet: An Information-Rich 3D Model Repositorycited by 2,880
  8. 2020Implicit Neural Representations with Periodic Activation Functionscited by 2,214
  9. 2019Normalizing Flows for Probabilistic Modeling and Inferencecited by 1,315
  10. 2021Towards Total Recall in Industrial Anomaly Detectioncited by 772
  11. 2020PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localizationcited by 591
  12. 2021CutPaste: Self-Supervised Learning for Anomaly Detection and Localizationcited by 529
28 more
  1. 2021DRAEM -- A discriminatively trained reconstruction embedding for surface anomaly detectioncited by 510
  2. 2022Masked Autoencoders for Point Cloud Self-supervised Learningcited by 456
  3. 2022Anomaly Detection via Reverse Distillation from One-Class Embeddingcited by 419
  4. 2019Uninformed Students: Student-Teacher Anomaly Detection with Discriminative Latent Embeddingscited by 386
  5. 2021CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flowscited by 320
  6. 2020Sub-Image Anomaly Detection with Deep Pyramid Correspondencescited by 297
  7. 2020Multiresolution Knowledge Distillation for Anomaly Detectioncited by 285
  8. 2018Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoderscited by 263
  9. 2021FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing Flowscited by 225
  10. 2020Patch SVDD: Patch-level SVDD for Anomaly Detection and Segmentationcited by 212
  11. 2020Same Same But DifferNet: Semi-Supervised Defect Detection with Normalizing Flowscited by 212
  12. 2020PANDA: Adapting Pretrained Features for Anomaly Detection and Segmentationcited by 175
  13. 2021The MVTec 3D-AD Dataset for Unsupervised 3D Anomaly Detection and Localizationcited by 174
  14. 2023Deep Industrial Image Anomaly Detection: A Surveycited by 147
  15. 2021Student-Teacher Feature Pyramid Matching for Anomaly Detectioncited by 145
  16. 2023Multimodal Industrial Anomaly Detection via Hybrid Fusioncited by 138
  17. 2020Learning and Evaluating Representations for Deep One-class Classificationcited by 133
  18. 2021Self-Supervised Predictive Convolutional Attentive Block for Anomaly Detectioncited by 119
  19. 2020Modeling the Distribution of Normal Data in Pre-Trained Deep Features for Anomaly Detectioncited by 115
  20. 2022Asymmetric Student-Teacher Networks for Industrial Anomaly Detectioncited by 115
  21. 2020Deep Nearest Neighbor Anomaly Detectioncited by 103
  22. 2022Back to the Feature: Classical 3D Features are (Almost) All You Need for 3D Anomaly Detectioncited by 92
  23. 2021Inpainting Transformer for Anomaly Detectioncited by 91
  24. 2021Divide-and-Assemble: Learning Block-wise Memory for Unsupervised Anomaly Detectioncited by 83
  25. 2022MemSeg: A semi-supervised method for image surface defect detection using differences and commonalitiescited by 68
  26. 2022The Eyecandies Dataset for Unsupervised Multimodal Anomaly Detection and Localizationcited by 55
  27. 2020DFR: Deep Feature Reconstruction for Unsupervised Anomaly Segmentationcited by 53
  28. 2020MOCCA: Multi-Layer One-Class ClassificAtion for Anomaly Detectioncited by 31

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 5; 2025: 16; 2026: 20.

41 papers.

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  1. 2025Taming Anomalies with Down-Up Sampling Networks: Group Center Preserving Reconstruction for 3D Anomaly Detectioncited by 11
  2. 2025Normal-Abnormal Guided Generalist Anomaly Detectioncited by 10
  3. 2025Anomaly Detection and Generation with Diffusion Models: A Surveycited by 8
  4. 2024Incomplete Multimodal Industrial Anomaly Detection via Cross-Modal Distillationcited by 6
  5. 2025SiM3D: Single-instance Multiview Multimodal and Multisetup 3D Anomaly Detection Benchmarkcited by 5
  6. 2024Test Time Training for Industrial Anomaly Segmentationcited by 4
  7. 2025Exploiting Point-Language Models with Dual-Prompts for 3D Anomaly Detectioncited by 4
  8. 20252D_3D Feature Fusion via Cross-Modal Latent Synthesis and Attention Guided Restoration for Industrial Anomaly Detectioncited by 4
  9. 2026GS-CLIP: Zero-shot 3D Anomaly Detection by Geometry-Aware Prompt and Synergistic View Representation Learningcited by 4
  10. 2026Towards an Incremental Unified Multimodal Anomaly Detection: Augmenting Multimodal Denoising From an Information Bottleneck Perspectivecited by 4
  11. 2025IEC3D-AD: A 3D Dataset of Industrial Equipment Components for Unsupervised Point Cloud Anomaly Detectioncited by 3
  12. 2025MuSc-V2: Zero-Shot Multimodal Industrial Anomaly Classification and Segmentation with Mutual Scoring of Unlabeled Samplescited by 3
29 more
  1. 2026Towards Multimodal Time Series Anomaly Detection with Semantic Alignment and Condensed Interactioncited by 3
  2. 2026Hierarchical Point-Patch Fusion with Adaptive Patch Codebook for 3D Shape Anomaly Detectioncited by 3
  3. 2025A Comprehensive Survey for Real-World Industrial Defect Detection: Challenges, Approaches, and Prospectscited by 2
  4. 2025MCL-AD: Multimodal Collaboration Learning for Zero-Shot 3D Anomaly Detectioncited by 2
  5. 2025Rethinking Efficient Mixture-of-Experts for Remote Sensing Modality-Missing Classificationcited by 2
  6. 2024Memoryless Multimodal Anomaly Detection via Student-Teacher Network and Signed Distance Learningcited by 1
  7. 2025A 3D Multimodal Feature for Infrastructure Anomaly Detectioncited by 1
  8. 2025Resilient Multimodal Industrial Surface Defect Detection with Uncertain Sensors Availabilitycited by 1
  9. 2025Point Cloud Segmentation of Integrated Circuits Package Substrates Surface Defects Using Causal Inference: Dataset Construction and Methodologycited by 1
  10. 2026Cross-Modal Mapping and Dual-Branch Reconstruction for 2D-3D Multimodal Industrial Anomaly Detectioncited by 1
  11. 2026Multimodal Industrial Anomaly Detection via Geometric Priorcited by 1
  12. 2024Learning to Be a Transformer to Pinpoint Anomaliescited by 0
  13. 2024Tuned Reverse Distillation: Enhancing Multimodal Industrial Anomaly Detection with Crossmodal Tunerscited by 0
  14. 2025Mentor3AD: Feature Reconstruction-based 3D Anomaly Detection via Multi-modality Mentor Learningcited by 0
  15. 2025Physic-HM: Restoring Physical Generative Logic in Multimodal Anomaly Detection via Hierarchical Modulationcited by 0
  16. 2026Test-Time Adaptation for Anomaly Segmentation via Topology-Aware Optimal Transport Chainingcited by 0
  17. 2026Multimodal Learning for Arcing Detection in Pantograph-Catenary Systemscited by 0
  18. 2026Modulate-and-Map: Crossmodal Feature Mapping with Cross-View Modulation for 3D Anomaly Detectioncited by 0
  19. 2026SGANet: Semantic and Geometric Alignment for Multimodal Multi-view Anomaly Detectioncited by 0
  20. 2026Out of Context: Reliability in Multimodal Anomaly Detection Requires Contextual Inferencecited by 0
  21. 2026Text-Guided Multimodal Unified Industrial Anomaly Detectioncited by 0
  22. 2026Parameter Efficient Multi-Class Intelligent Scheduling for Multimodal Online Distributed Industrial Anomaly Detectioncited by 0
  23. 2026Uni-RCM: Unified Reference-guided Cross-modal Mapping for Multi-Class Anomaly Detectioncited by 0
  24. 2026CMDS-AD: Cross-Modal Dual-Stream Decoupling for Few-Shot Anomaly Detectioncited by 0
  25. 2026Learning Topology-Aware Representations via Test-Time Adaptation for Anomaly Segmentationcited by 0
  26. 2026TC-MAF: Train-Calibrated Bounded Multi-Evidence Fusion for Multimodal Industrial Anomaly Detectioncited by 0
  27. 2026Understanding and Overcoming Cross-modal Fusion Bias in Multimodal Anomaly Detection From A Fisher Information Perspectivecited by 0
  28. 2026LIBAD: A Multimodal Anomaly Detection Benchmark for Li-Ion Battery Electrode Manufacturingcited by 0
  29. 2026Multi-Modal Anomaly Detection: A Surveycited by 0