ROADS: Robust Prompt-driven Multi-Class Anomaly Detection under Domain Shift

• URL: http://arxiv.org/abs/2411.16049v1
• Date: Mon, 25 Nov 2024 02:34:40 GMT
• Title: ROADS: Robust Prompt-driven Multi-Class Anomaly Detection under Domain Shift
• Authors: Hossein Kashiani, Niloufar Alipour Talemi, Fatemeh Afghah,
• Abstract summary: ROADS employs a hierarchical class-aware prompt integration mechanism to mitigate interference among anomaly classes. Experiments on MVTec-AD and VISA datasets demonstrate that ROADS surpasses state-of-the-art methods in both anomaly detection and localization.
• Score: 5.492174268132387
• License:
• Abstract: Recent advancements in anomaly detection have shifted focus towards Multi-class Unified Anomaly Detection (MUAD), offering more scalable and practical alternatives compared to traditional one-class-one-model approaches. However, existing MUAD methods often suffer from inter-class interference and are highly susceptible to domain shifts, leading to substantial performance degradation in real-world applications. In this paper, we propose a novel robust prompt-driven MUAD framework, called ROADS, to address these challenges. ROADS employs a hierarchical class-aware prompt integration mechanism that dynamically encodes class-specific information into our anomaly detector to mitigate interference among anomaly classes. Additionally, ROADS incorporates a domain adapter to enhance robustness against domain shifts by learning domain-invariant representations. Extensive experiments on MVTec-AD and VISA datasets demonstrate that ROADS surpasses state-of-the-art methods in both anomaly detection and localization, with notable improvements in out-of-distribution settings.

Related papers

• Generalize or Detect? Towards Robust Semantic Segmentation Under Multiple Distribution Shifts [56.57141696245328]
  In open-world scenarios, where both novel classes and domains may exist, an ideal segmentation model should detect anomaly classes for safety. Existing methods often struggle to distinguish between domain-level and semantic-level distribution shifts.
  arXiv  Detail & Related papers  (2024-11-06T11:03:02Z)
• DATR: Unsupervised Domain Adaptive Detection Transformer with Dataset-Level Adaptation and Prototypical Alignment [7.768332621617199]
  We introduce a strong DETR-based detector named Domain Adaptive detection TRansformer ( DATR) for unsupervised domain adaptation of object detection. Our proposed DATR incorporates a mean-teacher based self-training framework, utilizing pseudo-labels generated by the teacher model to further mitigate domain bias. Experiments demonstrate superior performance and generalization capabilities of our proposed DATR in multiple domain adaptation scenarios.
  arXiv  Detail & Related papers  (2024-05-20T03:48:45Z)
• DACAD: Domain Adaptation Contrastive Learning for Anomaly Detection in Multivariate Time Series [25.434379659643707]
  In time series anomaly detection, the scarcity of labeled data poses a challenge to the development of accurate models. We propose a novel Domain Contrastive learning model for Anomaly Detection in time series (DACAD) Our model employs supervised contrastive loss for the source domain and self-supervised contrastive triplet loss for the target domain.
  arXiv  Detail & Related papers  (2024-04-17T11:20:14Z)
• Toward Multi-class Anomaly Detection: Exploring Class-aware Unified Model against Inter-class Interference [67.36605226797887]
  We introduce a Multi-class Implicit Neural representation Transformer for unified Anomaly Detection (MINT-AD) By learning the multi-class distributions, the model generates class-aware query embeddings for the transformer decoder. MINT-AD can project category and position information into a feature embedding space, further supervised by classification and prior probability loss functions.
  arXiv  Detail & Related papers  (2024-03-21T08:08:31Z)
• Revisiting the Domain Shift and Sample Uncertainty in Multi-source Active Domain Transfer [69.82229895838577]
  Active Domain Adaptation (ADA) aims to maximally boost model adaptation in a new target domain by actively selecting a limited number of target data to annotate. This setting neglects the more practical scenario where training data are collected from multiple sources. This motivates us to target a new and challenging setting of knowledge transfer that extends ADA from a single source domain to multiple source domains.
  arXiv  Detail & Related papers  (2023-11-21T13:12:21Z)
• Robust Domain Adaptive Object Detection with Unified Multi-Granularity Alignment [59.831917206058435]
  Domain adaptive detection aims to improve the generalization of detectors on target domain. Recent approaches achieve domain adaption through feature alignment in different granularities via adversarial learning. We introduce a unified multi-granularity alignment (MGA)-based detection framework for domain-invariant feature learning.
  arXiv  Detail & Related papers  (2023-01-01T08:38:07Z)
• Decompose to Adapt: Cross-domain Object Detection via Feature Disentanglement [79.2994130944482]
  We design a Domain Disentanglement Faster-RCNN (DDF) to eliminate the source-specific information in the features for detection task learning. Our DDF method facilitates the feature disentanglement at the global and local stages, with a Global Triplet Disentanglement (GTD) module and an Instance Similarity Disentanglement (ISD) module. By outperforming state-of-the-art methods on four benchmark UDA object detection tasks, our DDF method is demonstrated to be effective with wide applicability.
  arXiv  Detail & Related papers  (2022-01-06T05:43:01Z)
• AFAN: Augmented Feature Alignment Network for Cross-Domain Object Detection [90.18752912204778]
  Unsupervised domain adaptation for object detection is a challenging problem with many real-world applications. We propose a novel augmented feature alignment network (AFAN) which integrates intermediate domain image generation and domain-adversarial training. Our approach significantly outperforms the state-of-the-art methods on standard benchmarks for both similar and dissimilar domain adaptations.
  arXiv  Detail & Related papers  (2021-06-10T05:01:20Z)

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.