Pith. sign in

REVIEW 4 cited by

Segment Any Anomaly without Training via Hybrid Prompt Regularization

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

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

Segment Any Anomaly without Training via Hybrid Prompt Regularization

Yunkang Cao , Xiaohao Xu , Chen Sun , Yuqi Cheng , Zongwei Du , Liang Gao , Weiming Shen This is my paper
classification cs.CV cs.AI
keywords anomalysegmentationfoundationhybridmodelsregularizationsegmentzero-shot
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
0 comments
read the original abstract

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}.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 4 Pith papers

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

  1. AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors

    cs.CV 2026-01 conditional novelty 7.0

    AnomalyVFM converts vision foundation models into zero-shot anomaly detectors via three-stage synthetic dataset generation plus low-rank adapters and weighted pixel loss, reaching 94.1% average image AUROC across nine...

  2. Closed form perturbative relativistic modifications to wave-packet dynamics in the quantum harmonic oscillator

    quant-ph 2026-03 unverdicted novelty 5.0

    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. On Efficient Variants of Segment Anything Model: A Survey

    cs.CV 2024-10 unverdicted novelty 5.0

    A survey that reviews efficient variants of the Segment Anything Model, categorizes acceleration strategies, and provides a unified hardware evaluation on benchmarks.

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

    cs.CV 2025-11 conditional novelty 4.0

    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.