MuSc-V2：基于无标签样本互评的零样本多模态工业异常分类与分割

MuSc-V2: Zero-Shot Multimodal Industrial Anomaly Classification and Segmentation with Mutual Scoring of Unlabeled Samples

November 13, 2025

作者: Xurui Li, Feng Xue, Yu Zhou

cs.AI

摘要

零样本异常分类（AC）与分割（AS）方法旨在无需任何标注样本的情况下识别并勾勒缺陷。本文揭示了现有方法忽视的关键特性：工业产品中的正常图像块通常能在二维外观和三维形状上找到大量相似匹配，而异常则保持多样性和孤立性。为显式利用这一判别特性，我们提出用于零样本AC/AS的互评分框架MuSc-V2，该框架灵活支持单模态（2D/3D）或多模态场景。具体而言，我们首先通过迭代点分组（IPG）优化三维表征，降低非连续表面导致的误判；继而采用多阶相似性邻域聚合（SNAMD）将2D/3D邻域线索融合为判别性更强的多尺度图像块特征用于互评分。核心机制包括：允许同模态样本相互评分的互评分机制（MSM），以及融合二维与三维分数以补全模态特异性缺失异常的跨模态异常增强（CAE）。最后，基于约束邻域的再评分（RsCon）通过比对更具代表性的样本抑制误分类。本框架可灵活应用于完整数据集或较小子集，并保持稳定性能，确保跨产品线的无缝适配。依托创新架构，MuSc-V2实现显著性能提升：在MVTec 3D-AD数据集上平均精度（AP）提升23.7%，在Eyecandies数据集上提升19.3%，超越现有零样本基准甚至多数少样本方法。代码将发布于https://github.com/HUST-SLOW/MuSc-V2。

English

Zero-shot anomaly classification (AC) and segmentation (AS) methods aim to identify and outline defects without using any labeled samples. In this paper, we reveal a key property that is overlooked by existing methods: normal image patches across industrial products typically find many other similar patches, not only in 2D appearance but also in 3D shapes, while anomalies remain diverse and isolated. To explicitly leverage this discriminative property, we propose a Mutual Scoring framework (MuSc-V2) for zero-shot AC/AS, which flexibly supports single 2D/3D or multimodality. Specifically, our method begins by improving 3D representation through Iterative Point Grouping (IPG), which reduces false positives from discontinuous surfaces. Then we use Similarity Neighborhood Aggregation with Multi-Degrees (SNAMD) to fuse 2D/3D neighborhood cues into more discriminative multi-scale patch features for mutual scoring. The core comprises a Mutual Scoring Mechanism (MSM) that lets samples within each modality to assign score to each other, and Cross-modal Anomaly Enhancement (CAE) that fuses 2D and 3D scores to recover modality-specific missing anomalies. Finally, Re-scoring with Constrained Neighborhood (RsCon) suppresses false classification based on similarity to more representative samples. Our framework flexibly works on both the full dataset and smaller subsets with consistently robust performance, ensuring seamless adaptability across diverse product lines. In aid of the novel framework, MuSc-V2 achieves significant performance improvements: a +23.7% AP gain on the MVTec 3D-AD dataset and a +19.3% boost on the Eyecandies dataset, surpassing previous zero-shot benchmarks and even outperforming most few-shot methods. The code will be available at The code will be available at https://github.com/HUST-SLOW/MuSc-V2{https://github.com/HUST-SLOW/MuSc-V2}.