mlsanityData to 14 Oct 2026

Continual-MEGA: A Large-scale Benchmark for Generalizable Continual Anomaly Detection

arXiv 2506.00956 ↗First posted 1 Jun 2025cs.CVcited by 4

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Abstract (arXiv)

In this paper, we introduce a new benchmark for continual learning in anomaly detection, aimed at better reflecting real-world deployment scenarios. Our benchmark, Continual-MEGA, includes a large and diverse dataset that significantly expands existing evaluation settings by combining carefully curated existing datasets with our newly proposed dataset, ContinualAD. In addition to standard continual learning with expanded quantity, we propose a novel scenario that measures zero-shot generalization to unseen classes, those not observed during continual adaptation. This setting poses a new problem setting that continual adaptation also enhances zero-shot performance. We also present a unified baseline algorithm that improves robustness in few-shot detection and maintains strong generalization. Through extensive evaluations, we report three key findings: (1) existing methods show substantial room for improvement, particularly in pixel-level defect localization; (2) our proposed method consistently outperforms prior approaches; and (3) the newly introduced ContinualAD dataset enhances the performance of strong anomaly detection models. We release the benchmark and code in https://github.com/Continual-Mega/Continual-Mega.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 51 of its 70 references that are in the map. First posted on arXiv 1 Jun 2025.

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51 of 70 references.

References · 51

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38 more
  1. 2020A Unifying Review of Deep and Shallow Anomaly Detectioncited by 425
  2. 2022Anomaly Detection via Reverse Distillation from One-Class Embeddingcited by 421
  3. 2022SPot-the-Difference Self-Supervised Pre-training for Anomaly Detection and Segmentationcited by 400
  4. 2019Deep Learning for Anomaly Detection: A Surveycited by 358
  5. 2021CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flowscited by 321
  6. 2023WinCLIP: Zero-/Few-Shot Anomaly Classification and Segmentationcited by 315
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  15. 2024Real-IAD: A Real-World Multi-View Dataset for Benchmarking Versatile Industrial Anomaly Detectioncited by 139
  16. 2023APRIL-GAN: A Zero-/Few-Shot Anomaly Classification and Segmentation Method for CVPR 2023 VAND Workshop Challenge Tracks 1&2: 1st Place on Zero-shot AD and 4th Place on Few-shot ADcited by 130
  17. 2024AdaCLIP: Adapting CLIP with Hybrid Learnable Prompts for Zero-Shot Anomaly Detectioncited by 127
  18. 2021Self-Supervised Predictive Convolutional Attentive Block for Anomaly Detectioncited by 119
  19. 2024PromptAD: Learning Prompts with only Normal Samples for Few-Shot Anomaly Detectioncited by 98
  20. 2024Toward Generalist Anomaly Detection via In-context Residual Learning with Few-shot Sample Promptscited by 89
  21. 2024A Unified Anomaly Synthesis Strategy with Gradient Ascent for Industrial Anomaly Detection and Localizationcited by 74
  22. 2024Adapting Visual-Language Models for Generalizable Anomaly Detection in Medical Imagescited by 69
  23. 2023CLIP-AD: A Language-Guided Staged Dual-Path Model for Zero-shot Anomaly Detectioncited by 58
  24. 2024FiLo: Zero-Shot Anomaly Detection by Fine-Grained Description and High-Quality Localizationcited by 51
  25. 2024VCP-CLIP: A visual context prompting model for zero-shot anomaly segmentationcited by 50
  26. 2024MediCLIP: Adapting CLIP for Few-shot Medical Image Anomaly Detectioncited by 35
  27. 2024Unsupervised Continual Anomaly Detection with Contrastively-learned Promptcited by 31
  28. 2024GeneralAD: Anomaly Detection Across Domains by Attending to Distorted Featurescited by 29
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  30. 2023An Incremental Unified Framework for Small Defect Inspectioncited by 25
  31. 2024ResAD: A Simple Framework for Class Generalizable Anomaly Detectioncited by 25
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  33. 2023Random Word Data Augmentation with CLIP for Zero-Shot Anomaly Detectioncited by 16
  34. 2024Kernel-Aware Graph Prompt Learning for Few-Shot Anomaly Detectioncited by 15
  35. 2024Unveiling the Anomalies in an Ever-Changing World: A Benchmark for Pixel-Level Anomaly Detection in Continual Learningcited by 13
  36. 2024Hyperparameters in Continual Learning: A Reality Checkcited by 12
  37. 2021Generative Replay-based Continual Zero-Shot Learningcited by 7
  38. 2024ONER: Online Experience Replay for Incremental Anomaly Detectioncited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 4.

4 papers.

  1. 2026Towards an Incremental Unified Multimodal Anomaly Detection: Augmenting Multimodal Denoising From an Information Bottleneck Perspectivecited by 4
  2. 2026Rethinking Continual Anomaly Detection on the Edge: Benchmarking Under Realistic Industrial Conditionscited by 0
  3. 2026Towards Principled Continual Anomaly Detection: A Systematic Framework and Benchmark Scenarioscited by 0
  4. 2026TED:Text-Axis Evidence Decomposition for Prompted Anomaly Localizationcited by 0