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

• URL: http://arxiv.org/abs/2506.00956v2
• Date: Thu, 31 Jul 2025 08:31:51 GMT
• Title: Continual-MEGA: A Large-scale Benchmark for Generalizable Continual Anomaly Detection
• Authors: Geonu Lee, Yujeong Oh, Geonhui Jang, Soyoung Lee, Jeonghyo Song, Sungmin Cha, YoungJoon Yoo,
• Abstract summary: We introduce a new benchmark for continual learning in anomaly detection, aimed at better reflecting real-world deployment scenarios.<n>Our benchmark, Continual-MEGA, includes a large and diverse dataset that significantly expands existing evaluation settings.<n>We propose a novel scenario that measures zero-shot generalization to unseen classes, those not observed during continual adaptation.
• Score: 11.416875086993139
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Rethinking Metrics and Benchmarks of Video Anomaly Detection [12.500876355560184]
  Video Anomaly Detection (VAD) aims to detect anomalies that deviate from expectation.<n>In this paper, we rethink VAD evaluation protocols through comprehensive experimental analyses.<n>We propose three novel evaluation methods to address these limitations.
  arXiv  Detail & Related papers  (2025-05-25T08:09:42Z)
• Exploring Generalized Gait Recognition: Reducing Redundancy and Noise within Indoor and Outdoor Datasets [24.242460774158463]
  Generalized gait recognition aims to achieve robust performance across diverse domains.<n>Mixed-dataset training is widely used to enhance generalization.<n>We propose a unified framework that systematically improves cross-domain gait recognition.
  arXiv  Detail & Related papers  (2025-05-21T06:46:09Z)
• Unified Domain Generalization and Adaptation for Multi-View 3D Object Detection [14.837853049121687]
  3D object detection leveraging multi-view cameras has demonstrated their practical and economical value in challenging vision tasks. Typical supervised learning approaches face challenges in achieving satisfactory adaptation toward unseen and unlabeled target datasets. We propose Unified Domain Generalization and Adaptation (UDGA), a practical solution to mitigate those drawbacks.
  arXiv  Detail & Related papers  (2024-10-29T18:51:49Z)
• Enhancement of Approximation Spaces by the Use of Primals and Neighborhood [0.0]
  We introduce four new generalized rough set models that draw inspiration from "neighborhoods and primals" We claim that the current models can preserve nearly all significant aspects associated with the rough set model. We also demonstrate that the new strategy we define for our everyday health-related problem yields more accurate findings.
  arXiv  Detail & Related papers  (2024-10-23T18:49:13Z)
• Learning Feature Inversion for Multi-class Anomaly Detection under General-purpose COCO-AD Benchmark [101.23684938489413]
  Anomaly detection (AD) is often focused on detecting anomalies for industrial quality inspection and medical lesion examination. This work first constructs a large-scale and general-purpose COCO-AD dataset by extending COCO to the AD field. Inspired by the metrics in the segmentation field, we propose several more practical threshold-dependent AD-specific metrics.
  arXiv  Detail & Related papers  (2024-04-16T17:38:26Z)
• Small Object Detection via Coarse-to-fine Proposal Generation and Imitation Learning [52.06176253457522]
  We propose a two-stage framework tailored for small object detection based on the Coarse-to-fine pipeline and Feature Imitation learning. CFINet achieves state-of-the-art performance on the large-scale small object detection benchmarks, SODA-D and SODA-A.
  arXiv  Detail & Related papers  (2023-08-18T13:13:09Z)
• Revisiting Consistency Regularization for Semi-Supervised Learning [80.28461584135967]
  We propose an improved consistency regularization framework by a simple yet effective technique, FeatDistLoss. Experimental results show that our model defines a new state of the art for various datasets and settings.
  arXiv  Detail & Related papers  (2021-12-10T20:46:13Z)
• Open-Set Recognition: A Good Closed-Set Classifier is All You Need [146.6814176602689]
  We show that the ability of a classifier to make the 'none-of-above' decision is highly correlated with its accuracy on the closed-set classes. We use this correlation to boost the performance of the cross-entropy OSR 'baseline' by improving its closed-set accuracy. We also construct new benchmarks which better respect the task of detecting semantic novelty.
  arXiv  Detail & Related papers  (2021-10-12T17:58:59Z)
• Semi-Supervised Domain Generalization with Stochastic StyleMatch [90.98288822165482]
  In real-world applications, we might have only a few labels available from each source domain due to high annotation cost. In this work, we investigate semi-supervised domain generalization, a more realistic and practical setting. Our proposed approach, StyleMatch, is inspired by FixMatch, a state-of-the-art semi-supervised learning method based on pseudo-labeling.
  arXiv  Detail & Related papers  (2021-06-01T16:00:08Z)
• Squared $\ell_2$ Norm as Consistency Loss for Leveraging Augmented Data to Learn Robust and Invariant Representations [76.85274970052762]
  Regularizing distance between embeddings/representations of original samples and augmented counterparts is a popular technique for improving robustness of neural networks. In this paper, we explore these various regularization choices, seeking to provide a general understanding of how we should regularize the embeddings. We show that the generic approach we identified (squared $ell$ regularized augmentation) outperforms several recent methods, which are each specially designed for one task.
  arXiv  Detail & Related papers  (2020-11-25T22:40:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.