mlsanityData to 7 Oct 2026

Tuned Reverse Distillation: Enhancing Multimodal Industrial Anomaly Detection with Crossmodal Tuners

arXiv 2412.08949 ↗First posted 12 Dec 2024cs.CVcited by 0

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Abstract (arXiv)

Knowledge distillation (KD) has been widely studied in unsupervised image Anomaly Detection (AD), but its application to unsupervised multimodal AD remains underexplored. Existing KD-based methods for multimodal AD that use fused multimodal features to obtain teacher representations face challenges. Anomalies that only exist in one modality may not be effectively captured in the fused teacher features, leading to detection failures. Besides, these methods do not fully leverage the rich intra- and inter-modality information that are critical for effective anomaly detection. In this paper, we propose Tuned Reverse Distillation (TRD) based on Multi-branch design to realize Multimodal Industrial AD. By assigning independent branches to each modality, our method enables finer detection of anomalies within each modality. Furthermore, we enhance the interaction between modalities during the distillation process by designing two Crossmodal Tuners including Crossmodal Filter and Amplifier. With the idea of crossmodal mapping, the student network is allowed to better learn normal features while anomalies in all modalities are ensured to be effectively detected. Experimental verifications on multimodal AD datasets demonstrate that our method achieves state-of-the-art performance in multimodal anomaly detection and localization. Code is available at https://github.com/hito2448/TRD.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 29 of its 45 references that are in the map. First posted on arXiv 12 Dec 2024.

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29 of 45 references.

References · 29

  1. 2017Attention Is All You Needcited by 59,932
  2. 2016Wide Residual Networkscited by 3,057
  3. 2020Transformer Feed-Forward Layers Are Key-Value Memoriescited by 1,307
  4. 2021Towards Total Recall in Industrial Anomaly Detectioncited by 772
  5. 2021CutPaste: Self-Supervised Learning for Anomaly Detection and Localizationcited by 529
  6. 2021DRAEM -- A discriminatively trained reconstruction embedding for surface anomaly detectioncited by 510
  7. 2022Anomaly Detection via Reverse Distillation from One-Class Embeddingcited by 419
  8. 2018GANomaly: Semi-Supervised Anomaly Detection via Adversarial Trainingcited by 409
  9. 2022SPot-the-Difference Self-Supervised Pre-training for Anomaly Detection and Segmentationcited by 397
  10. 2020Multiresolution Knowledge Distillation for Anomaly Detectioncited by 285
  11. 2022A Unified Model for Multi-class Anomaly Detectioncited by 267
  12. 2021The MVTec 3D-AD Dataset for Unsupervised 3D Anomaly Detection and Localizationcited by 174
17 more
  1. 2023Multimodal Industrial Anomaly Detection via Hybrid Fusioncited by 138
  2. 2022Asymmetric Student-Teacher Networks for Industrial Anomaly Detectioncited by 115
  3. 2022Back to the Feature: Classical 3D Features are (Almost) All You Need for 3D Anomaly Detectioncited by 92
  4. 2023Real3D-AD: A Dataset of Point Cloud Anomaly Detectioncited by 87
  5. 2022PNI : Industrial Anomaly Detection using Position and Neighborhood Informationcited by 69
  6. 2022Anomaly Detection in 3D Point Clouds using Deep Geometric Descriptorscited by 66
  7. 2023ReContrast: Domain-Specific Anomaly Detection via Contrastive Reconstructioncited by 62
  8. 2022The Eyecandies Dataset for Unsupervised Multimodal Anomaly Detection and Localizationcited by 55
  9. 2022Self-Supervised Masking for Unsupervised Anomaly Detection and Localizationcited by 43
  10. 2023EasyNet: An Easy Network for 3D Industrial Anomaly Detectioncited by 43
  11. 2023TransFusion -- A Transparency-Based Diffusion Model for Anomaly Detectioncited by 43
  12. 2023Multimodal Industrial Anomaly Detection by Crossmodal Feature Mappingcited by 41
  13. 2023Cheating Depth: Enhancing 3D Surface Anomaly Detection via Depth Simulationcited by 33
  14. 2023Target before Shooting: Accurate Anomaly Detection and Localization under One Millisecond via Cascade Patch Retrievalcited by 22
  15. 2023Contextual Affinity Distillation for Image Anomaly Detectioncited by 18
  16. 2024Self-supervised Feature Adaptation for 3D Industrial Anomaly Detectioncited by 16
  17. 2023A Comprehensive Augmentation Framework for Anomaly Detectioncited by 8

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