mlsanityData to 14 Oct 2026

AnomalyNCD: Towards Novel Anomaly Class Discovery in Industrial Scenarios

arXiv 2410.14379 ↗First posted 18 Oct 2024cs.CVcited by 9

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Abstract (arXiv)

Recently, multi-class anomaly classification has garnered increasing attention. Previous methods directly cluster anomalies but often struggle due to the lack of anomaly-prior knowledge. Acquiring this knowledge faces two issues: the non-prominent and weak-semantics anomalies. In this paper, we propose AnomalyNCD, a multi-class anomaly classification network compatible with different anomaly detection methods. To address the non-prominence of anomalies, we design main element binarization (MEBin) to obtain anomaly-centered images, ensuring anomalies are learned while avoiding the impact of incorrect detections. Next, to learn anomalies with weak semantics, we design mask-guided representation learning, which focuses on isolated anomalies guided by masks and reduces confusion from erroneous inputs through corrected pseudo labels. Finally, to enable flexible classification at both region and image levels, we develop a region merging strategy that determines the overall image category based on the classified anomaly regions. Our method outperforms the state-of-the-art works on the MVTec AD and MTD datasets. Compared with the current methods, AnomalyNCD combined with zero-shot anomaly detection method achieves a 10.8% F1 gain, 8.8% NMI gain, and 9.5% ARI gain on MVTec AD, and 12.8% F1 gain, 5.7% NMI gain, and 10.8% ARI gain on MTD. Code is available at https://github.com/HUST-SLOW/AnomalyNCD.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 54 of its 81 references that are in the map. First posted on arXiv 18 Oct 2024.

Builds on

54 of 81 references.

References · 54

  1. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  2. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 12,049
  3. 2018Representation Learning with Contrastive Predictive Codingcited by 7,777
  4. 2019Momentum Contrast for Unsupervised Visual Representation Learningcited by 7,513
  5. 2021Emerging Properties in Self-Supervised Vision Transformerscited by 6,968
  6. 2020Bootstrap your own latent: A new approach to self-supervised Learningcited by 4,750
  7. 2020Exploring Simple Siamese Representation Learningcited by 2,866
  8. 2020Unsupervised Learning of Visual Features by Contrasting Cluster Assignmentscited by 2,857
  9. 2020Supervised Contrastive Learningcited by 2,829
  10. 2021Masked-attention Mask Transformer for Universal Image Segmentationcited by 2,351
  11. 2019RandAugment: Practical automated data augmentation with a reduced search spacecited by 2,269
  12. 2019Self-training with Noisy Student improves ImageNet classificationcited by 1,311
38 more
  1. 2021Towards Total Recall in Industrial Anomaly Detectioncited by 776
  2. 2018Invariant Information Clustering for Unsupervised Image Classification and Segmentationcited by 455
  3. 2020SCAN: Learning to Classify Images without Labelscited by 352
  4. 2018Learning Pixel-level Semantic Affinity with Image-level Supervision for Weakly Supervised Semantic Segmentationcited by 321
  5. 2023WinCLIP: Zero-/Few-Shot Anomaly Classification and Segmentationcited by 315
  6. 2019Learning to Discover Novel Visual Categories via Deep Transfer Clusteringcited by 225
  7. 2022Generalized Category Discoverycited by 211
  8. 2021A Unified Objective for Novel Class Discoverycited by 162
  9. 2023DiffuMask: Synthesizing Images with Pixel-level Annotations for Semantic Segmentation Using Diffusion Modelscited by 162
  10. 2023EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latenciescited by 160
  11. 2017Learning to cluster in order to transfer across domains and taskscited by 128
  12. 2021AutoNovel: Automatically Discovering and Learning Novel Visual Categoriescited by 116
  13. 2022Parametric Classification for Generalized Category Discovery: A Baseline Studycited by 113
  14. 2019TANet: Robust 3D Object Detection from Point Clouds with Triple Attentioncited by 108
  15. 2021SPICE: Semantic Pseudo-labeling for Image Clusteringcited by 104
  16. 2019Multi-class Classification without Multi-class Labelscited by 100
  17. 2020OpenMix: Reviving Known Knowledge for Discovering Novel Visual Categories in An Open Worldcited by 93
  18. 2022Omni-frequency Channel-selection Representations for Unsupervised Anomaly Detectioncited by 80
  19. 2022PNI : Industrial Anomaly Detection using Position and Neighborhood Informationcited by 69
  20. 2023No Representation Rules Them All in Category Discoverycited by 61
  21. 2024MuSc: Zero-Shot Industrial Anomaly Classification and Segmentation with Mutual Scoring of the Unlabeled Imagescited by 52
  22. 2014DeepEdge: A Multi-Scale Bifurcated Deep Network for Top-Down Contour Detectioncited by 50
  23. 2020GATCluster: Self-Supervised Gaussian-Attention Network for Image Clusteringcited by 32
  24. 2021Novel Class Discovery in Semantic Segmentationcited by 31
  25. 2021Pseudo-supervised Deep Subspace Clusteringcited by 29
  26. 2023Unknown Sniffer for Object Detection: Don't Turn a Blind Eye to Unknown Objectscited by 23
  27. 2023Industrial Anomaly Detection with Domain Shift: A Real-world Dataset and Masked Multi-scale Reconstructioncited by 23
  28. 2023Class-relation Knowledge Distillation for Novel Class Discoverycited by 23
  29. 2023Target before Shooting: Accurate Anomaly Detection and Localization under One Millisecond via Cascade Patch Retrievalcited by 22
  30. 2023Novel Class Discovery for 3D Point Cloud Semantic Segmentationcited by 21
  31. 2022Fast Road Segmentation via Uncertainty-aware Symmetric Networkcited by 17
  32. 2019Occlusion-shared and Feature-separated Network for Occlusion Relationship Reasoningcited by 16
  33. 2019A Novel Multi-layer Framework for Tiny Obstacle Discoverycited by 14
  34. 2021Anomaly Clustering: Grouping Images into Coherent Clusters of Anomaly Typescited by 13
  35. 2023UniFormaly: Towards Task-Agnostic Unified Framework for Visual Anomaly Detectioncited by 11
  36. 2021Reference-based Defect Detection Networkcited by 10
  37. 2024Seeing Unseen: Discover Novel Biomedical Concepts via Geometry-Constrained Probabilistic Modelingcited by 10
  38. 2024Dual-level Adaptive Self-Labeling for Novel Class Discovery in Point Cloud Segmentationcited by 10

50 most cited of 54 All 54 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 3; 2026: 6.

9 papers.

  1. 2026Relational Retrieval: Leveraging Known-Novel Interactions for Generalized Category Discoverycited by 2
  2. 2025Image-Intrinsic Priors for Integrated Circuit Defect Detection and Novel Class Discovery via Self-Supervised Learningcited by 0
  3. 2025UniADC: A Unified Framework for Anomaly Detection and Classificationcited by 0
  4. 2025ProtoAnomalyNCD: Prototype Learning for Multi-class Novel Anomaly Discovery in Industrial Scenarioscited by 0
  5. 2026Interpretable Logical Anomaly Classification via Constraint Decomposition and Instruction Fine-Tuningcited by 0
  6. 2026The Devil Is in Gradient Entanglement: Energy-Aware Gradient Coordinator for Robust Generalized Category Discoverycited by 0
  7. 2026An Analysis Focused on Womens Safety: Can VAD Models Be Enhanced by a Multi-modal Dataset?cited by 0
  8. 2026CoGeoAD: Hierarchical Color-Geometric Fusion with Multi-View Attention for Zero-Shot 3D Anomaly Detectioncited by 0
  9. 2026Focus and Supplement: Dual-Enhanced Vision Transformer for Multi-Class Anomaly Classificationcited by 0