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Segment Any Anomaly without Training via Hybrid Prompt Regularization

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arxiv 2305.10724 v1 pith:WW46DJ26 submitted 2023-05-18 cs.CV cs.AI

Yunkang Cao , Xiaohao Xu , Chen Sun , Yuqi Cheng , Zongwei Du , Liang Gao , Weiming Shen This is my paper · ORCID
classification cs.CVcs.AI
keywords anomalysegmentationfoundationhybridmodelsregularizationsegmentzero-shot
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We present a novel framework, i.e., Segment Any Anomaly + (SAA+), for zero-shot anomaly segmentation with hybrid prompt regularization to improve the adaptability of modern foundation models. Existing anomaly segmentation models typically rely on domain-specific fine-tuning, limiting their generalization across countless anomaly patterns. In this work, inspired by the great zero-shot generalization ability of foundation models like Segment Anything, we first explore their assembly to leverage diverse multi-modal prior knowledge for anomaly localization. For non-parameter foundation model adaptation to anomaly segmentation, we further introduce hybrid prompts derived from domain expert knowledge and target image context as regularization. Our proposed SAA+ model achieves state-of-the-art performance on several anomaly segmentation benchmarks, including VisA, MVTec-AD, MTD, and KSDD2, in the zero-shot setting. We will release the code at \href{https://github.com/caoyunkang/Segment-Any-Anomaly}{https://github.com/caoyunkang/Segment-Any-Anomaly}.

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Forward citations

Cited by 8 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. IQE-CLIP: Instance-aware Query Embedding for Zero-/Few-shot Anomaly Detection in Medical Domain

    cs.CV 2025-06 conditional novelty 6.0 of 10

    IQE-CLIP improves zero- and few-shot medical anomaly detection by building query embeddings that combine text prompts with visual features from each test image, beating prior CLIP-based methods on six BMAD datasets.

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    Closed-form O(1/c²) relativistic corrections to QHO wave-packet widths, variances, and uncertainty products leave minimum-uncertainty saturation intact and become percent-level for 1–10 keV electron confinement.

  3. Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts

    cs.CV 2025-07 conditional novelty 5.0 of 10

    The paper proposes the long-tailed online anomaly detection (LTOAD) benchmark and a class-agnostic concept-based framework that outperforms class-aware baselines in most offline settings and in the online setting.

  4. Anomaly Object Segmentation with Vision-Language Models for Steel Scrap Recycling

    cs.CV 2025-06 conditional novelty 5.0 of 10

    Supervised finetuning of a CLIP image encoder with multi-scale features and text prompts detects anomaly objects in steel scrap at 28.6% pixel-level average precision, outperforming tested baselines on a private dataset.

  5. MIAS-SAM: Medical Image Anomaly Segmentation without thresholding

    cs.CV 2025-05 conditional novelty 5.0 of 10

    MIAS-SAM generates medical anomaly segmentations by memory-bank matching of SAM encoder features, using the anomaly map's center of gravity as a point prompt for the SAM decoder, without thresholding the anomaly map.

  6. OmniAD: Detect and Understand Industrial Anomaly via Multimodal Reasoning

    cs.CV 2025-05 reject novelty 5.0 of 10

    OmniAD unifies industrial anomaly detection and understanding in a single multimodal model using text-encoded masks and reinforcement learning, reporting 79.1 on MMAD and strong detection scores.

  7. SAM-MI: A Mask-Injected Framework for Enhancing Open-Vocabulary Semantic Segmentation with SAM

    cs.CV 2025-11 conditional novelty 4.0 of 10

    SAM-MI improves open-vocabulary segmentation by injecting aggregated SAM masks as low- and high-frequency guidance into CLIP cost maps, with sparse text-guided point prompts for speed.

  8. StackCLIP: Clustering-Driven Stacked Prompt in Zero-Shot Industrial Anomaly Detection

    cs.CV 2025-06 conditional novelty 4.0 of 10

    Stacking multiple category names in a CLIP text prompt, along with cluster-specific alignment layers, improves zero-shot industrial defect detection and localization.

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