Visual Prompting Meets Feature Reconstruction-Based Anomaly Detection with Dual-Teacher Supervision

• URL: http://arxiv.org/abs/2606.09670v1
• Date: Mon, 08 Jun 2026 15:52:05 GMT
• Title: Visual Prompting Meets Feature Reconstruction-Based Anomaly Detection with Dual-Teacher Supervision
• Abstract summary: We introduce a visual prompting pipeline that isolates objects using foreground-background masking.<n>We also introduce a mechanism for unfreezing the teacher in student-teacher models to improve domain adaptability.<n>We achieve a 3.5 percentage point improvement over the previous state-of-the-art on the challenging AeBAD dataset.
• Score: 7.548589503544904
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent Anomaly Detection methods achieve perfect detection and segmentation scores on well-established datasets, such as MVTec. However, many of these methods face challenges when foundational assumptions - such as consistent object scale, viewpoint, background, illumination, and centered placement - are violated. Those variations that occur render anomaly detection methods unusable in many real-world scenarios. To address these limitations, we introduce three key contributions: (1) a visual prompting pipeline that isolates objects using foreground-background masking; (2) a mechanism for unfreezing the teacher in student-teacher models to improve domain adaptability; and (3) a data augmentation strategy leveraging diffusion-generated synthetic images to enhance anomaly detection performance. We achieve a 3.5 percentage point improvement over the previous state-of-the-art on the challenging AeBAD dataset by using the Masked Multiscale Reconstruction (MMR) model as our backbone.

Related papers

• 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)
• AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors [6.6016630449883955]
  AnomalyVFM is a framework that turns any pretrained VFM into a strong zero-shot anomaly detector.<n>Our approach combines a robust three-stage synthetic dataset generation scheme with a parameter-efficient adaptation mechanism.<n>It achieves an average image-level AUROC of 94.1% across 9 diverse datasets, surpassing previous methods by significant 3.3 percentage points.
  arXiv  Detail & Related papers  (2026-01-28T12:02:58Z)
• Single-Step Reconstruction-Free Anomaly Detection and Segmentation via Diffusion Models [1.1487074612765584]
  We introduce Reconstruction-free Anomaly Detection with Attention-based diffusion models in Real-time (RADAR)<n>RADAR overcomes the limitations of reconstruction-based anomaly detection.<n>We evaluate RADAR on real-world 3D-printed material and the MVTec-AD dataset.
  arXiv  Detail & Related papers  (2025-08-06T18:56:08Z)
• Topology-Aware Modeling for Unsupervised Simulation-to-Reality Point Cloud Recognition [63.55828203989405]
  We introduce a novel Topology-Aware Modeling (TAM) framework for Sim2Real UDA on object point clouds.<n>Our approach mitigates the domain gap by leveraging global spatial topology, characterized by low-level, high-frequency 3D structures.<n>We propose an advanced self-training strategy that combines cross-domain contrastive learning with self-training.
  arXiv  Detail & Related papers  (2025-06-26T11:53:59Z)
• Towards Unified 3D Object Detection via Algorithm and Data Unification [70.27631528933482]
  We build the first unified multi-modal 3D object detection benchmark MM- Omni3D and extend the aforementioned monocular detector to its multi-modal version. We name the designed monocular and multi-modal detectors as UniMODE and MM-UniMODE, respectively.
  arXiv  Detail & Related papers  (2024-02-28T18:59:31Z)
• Self-supervised Feature Adaptation for 3D Industrial Anomaly Detection [59.41026558455904]
  We focus on multi-modal anomaly detection. Specifically, we investigate early multi-modal approaches that attempted to utilize models pre-trained on large-scale visual datasets. We propose a Local-to-global Self-supervised Feature Adaptation (LSFA) method to finetune the adaptors and learn task-oriented representation toward anomaly detection.
  arXiv  Detail & Related papers  (2024-01-06T07:30:41Z)
• DiAD: A Diffusion-based Framework for Multi-class Anomaly Detection [55.48770333927732]
  We propose a Difusion-based Anomaly Detection (DiAD) framework for multi-class anomaly detection. It consists of a pixel-space autoencoder, a latent-space Semantic-Guided (SG) network with a connection to the stable diffusion's denoising network, and a feature-space pre-trained feature extractor. Experiments on MVTec-AD and VisA datasets demonstrate the effectiveness of our approach.
  arXiv  Detail & Related papers  (2023-12-11T18:38:28Z)
• Uncertainty-Aware Adaptation for Self-Supervised 3D Human Pose Estimation [70.32536356351706]
  We introduce MRP-Net that constitutes a common deep network backbone with two output heads subscribing to two diverse configurations. We derive suitable measures to quantify prediction uncertainty at both pose and joint level. We present a comprehensive evaluation of the proposed approach and demonstrate state-of-the-art performance on benchmark datasets.
  arXiv  Detail & Related papers  (2022-03-29T07:14:58Z)
• PatchMVSNet: Patch-wise Unsupervised Multi-View Stereo for Weakly-Textured Surface Reconstruction [2.9896482273918434]
  This paper proposes robust loss functions leveraging constraints beneath multi-view images to alleviate matching ambiguity. Our strategy can be implemented with arbitrary depth estimation frameworks and can be trained with arbitrary large-scale MVS datasets. Our method reaches the performance of the state-of-the-art methods on popular benchmarks, like DTU, Tanks and Temples and ETH3D.
  arXiv  Detail & Related papers  (2022-03-04T07:05:23Z)
• Supervising Remote Sensing Change Detection Models with 3D Surface Semantics [1.8782750537161614]
  We propose Contrastive Surface-Image Pretraining (CSIP) for joint learning using optical RGB and above ground level (AGL) map pairs. We then evaluate these pretrained models on several building segmentation and change detection datasets to show that our method does, in fact, extract features relevant to downstream applications.
  arXiv  Detail & Related papers  (2022-02-26T23:35:43Z)

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.