Exploring Multimodal Prompts For Unsupervised Continuous Anomaly Detection

• URL: http://arxiv.org/abs/2603.21562v1
• Date: Mon, 23 Mar 2026 04:33:11 GMT
• Title: Exploring Multimodal Prompts For Unsupervised Continuous Anomaly Detection
• Abstract summary: We propose an unsupervised continual anomaly detection framework grounded in multimodal prompting.<n> Specifically, we introduce a Continual Multimodal Prompt Memory Bank (CMPMB) that progressively distills and retains normal patterns from both visual and textual domains.<n>We also devise a Defect-Semantic-Guided Adaptive Fusion Mechanism (DSG-AFM) that integrates an Adaptive Normalization Module (ANM) with a Dynamic Fusion Strategy (DFS) to jointly enhance detection accuracy and adversarial robustness.
• Score: 12.745823680495887
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Unsupervised Continuous Anomaly Detection (UCAD) is gaining attention for effectively addressing the catastrophic forgetting and heavy computational burden issues in traditional Unsupervised Anomaly Detection (UAD). However, existing UCAD approaches that rely solely on visual information are insufficient to capture the manifold of normality in complex scenes, thereby impeding further gains in anomaly detection accuracy. To overcome this limitation, we propose an unsupervised continual anomaly detection framework grounded in multimodal prompting. Specifically, we introduce a Continual Multimodal Prompt Memory Bank (CMPMB) that progressively distills and retains prototypical normal patterns from both visual and textual domains across consecutive tasks, yielding a richer representation of normality. Furthermore, we devise a Defect-Semantic-Guided Adaptive Fusion Mechanism (DSG-AFM) that integrates an Adaptive Normalization Module (ANM) with a Dynamic Fusion Strategy (DFS) to jointly enhance detection accuracy and adversarial robustness. Benchmark experiments on MVTec AD and VisA datasets show that our approach achieves state-of-the-art (SOTA) performance on image-level AUROC and pixel-level AUPR metrics.

Related papers

• XMatchAD: A Cross-Modal Matching Perspective on Reconstruction-based Anomaly Detection [30.401044523780723]
  XMatchAD is a novel UAD framework that reinterprets the task from a pseudo cross-modal matching perspective.<n>Our method consistently achieves superior performance, outperforming state-of-the-art methods in multi-class anomaly detection and localization.
  arXiv  Detail & Related papers  (2026-07-26T13:47:10Z)
• Text-Guided Multimodal Unified Industrial Anomaly Detection [71.95719669933312]
  We propose a unified multimodal industrial anomaly detection framework guided by text semantics.<n>The framework consists of two core modules: a Geometry-Aware Cross-Modal Mapper and an Object-Conditioned Textual Feature Adaptor.<n>Our method achieves state-of-the-art performance in classification and localization under unsupervised settings.
  arXiv  Detail & Related papers  (2026-04-24T13:21:22Z)
• VAN-AD: Visual Masked Autoencoder with Normalizing Flow For Time Series Anomaly Detection [17.972690703544764]
  Time series anomaly detection (TSAD) is essential for maintaining the reliability and security of IoT-enabled service systems.<n>In this paper, we investigate the applicability of large-scale vision models to TSAD.<n>We propose VAN-AD, a novel MAE-based framework for TSAD.
  arXiv  Detail & Related papers  (2026-03-27T08:48:59Z)
• Steering and Rectifying Latent Representation Manifolds in Frozen Multi-modal LLMs for Video Anomaly Detection [52.5174167737992]
  Video anomaly detection (VAD) aims to identify abnormal events in videos.<n>We propose SteerVAD, which advances MLLM-based VAD by shifting from passively reading to actively steering and rectifying internal representations.<n>Our method achieves state-of-the-art performance among tuning-free approaches requiring only 1% of training data.
  arXiv  Detail & Related papers  (2026-02-27T13:48:50Z)
• ABounD: Adversarial Boundary-Driven Few-Shot Learning for Multi-Class Anomaly Detection [24.691181948844136]
  ABversaounD is an Adrial Boundary-Driven few-shot learning framework for multi-class anomaly detection.<n>It integrates semantic concept learning with decision boundary shaping.<n>Experiments on MVTec-AD and VisA datasets demonstrate state-of-the-art performance.
  arXiv  Detail & Related papers  (2025-11-27T13:18:22Z)
• Unified Unsupervised Anomaly Detection via Matching Cost Filtering [113.43366521994396]
  Unsupervised anomaly detection (UAD) aims to identify image- and pixel-level anomalies using only normal training data.<n>We present Unified Cost Filtering (UCF), a generic post-hoc refinement framework for refining anomaly cost volume of any UAD model.
  arXiv  Detail & Related papers  (2025-10-03T03:28:18Z)
• Insight-LLM: LLM-enhanced Multi-view Fusion in Insider Threat Detection [9.040775027499265]
  We present Insight-LLM, the first modular multi-view fusion framework specifically tailored for insider threat detection.<n> Insight-LLM employs frozen, pre-nes, achieving state-of-the-art detection with low latency and parameter overhead.
  arXiv  Detail & Related papers  (2025-09-01T14:31:12Z)
• AMAD: AutoMasked Attention for Unsupervised Multivariate Time Series Anomaly Detection [0.7371521417300614]
  AMAD integrates textbfAutotextbfMasked Attention for UMTStextbfAD scenarios.<n>AMAD provides a robust and adaptable solution to UMTSAD challenges.
  arXiv  Detail & Related papers  (2025-04-09T07:32:59Z)
• Feature Attenuation of Defective Representation Can Resolve Incomplete Masking on Anomaly Detection [1.0358639819750703]
  In unsupervised anomaly detection (UAD) research, it is necessary to develop a computationally efficient and scalable solution. We revisit the reconstruction-by-inpainting approach and rethink to improve it by analyzing strengths and weaknesses. We propose Feature Attenuation of Defective Representation (FADeR) that only employs two layers which attenuates feature information of anomaly reconstruction.
  arXiv  Detail & Related papers  (2024-07-05T15:44:53Z)
• DACAD: Domain Adaptation Contrastive Learning for Anomaly Detection in Multivariate Time Series [83.76994646443498]
  In time series anomaly detection, the scarcity of labeled data poses a challenge to the development of accurate models.<n>We propose a novel Domain Contrastive learning model for Anomaly Detection in time series (DACAD)<n>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)
• DMAD: Dual Memory Bank for Real-World Anomaly Detection [90.97573828481832]
  We propose a new framework named Dual Memory bank enhanced representation learning for Anomaly Detection (DMAD) DMAD employs a dual memory bank to calculate feature distance and feature attention between normal and abnormal patterns. We evaluate DMAD on the MVTec-AD and VisA datasets.
  arXiv  Detail & Related papers  (2024-03-19T02:16:32Z)
• Unsupervised Continual Anomaly Detection with Contrastively-learned Prompt [80.43623986759691]
  We introduce a novel Unsupervised Continual Anomaly Detection framework called UCAD. The framework equips the UAD with continual learning capability through contrastively-learned prompts. We conduct comprehensive experiments and set the benchmark on unsupervised continual anomaly detection and segmentation.
  arXiv  Detail & Related papers  (2024-01-02T03:37:11Z)

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.