提出通用少样本缺陷分类模型，提升工业质检泛化能力。
MVREC: A General Few-shot Defect Classification Model Using Multi-View Region-Context
- 用AlphaCLIP提取通用缺陷特征，结合多视角上下文增强
- 在MVTec-FS上实现94.3%准确率，优于现有方法
- 适合工业质检中数据稀缺场景的快速部署
少样本缺陷多分类（FSDMC）是工业制造质量控制的新趋势。然而，现有研究常因局限于特定数据集而缺乏泛化性；同时，缺陷分类高度依赖图像上下文信息，现有方法难以有效提取。为此，我们提出通用的FSDMC框架MVREC，具备两大优势：(1) 通过预训练AlphaCLIP模型提取缺陷实例的通用特征；(2) 采用区域-上下文框架，利用掩码区域输入与多视角上下文增强来强化缺陷特征。模型还引入少样本Zip-Adapter(-F)分类器，缓存支持集视觉特征并实现少样本分类。我们构建了基于MVTec AD的新基准MVTec-FS，包含1228张带实例级掩码标注的缺陷图像和46种缺陷类型。在MVTec-FS及四个额外数据集上的大量实验表明，该模型在通用缺陷分类中表现优异，且能有效利用上下文信息提升分类性能。代码已开源。
原文摘要 · Abstract (English)
Few-shot defect multi-classification (FSDMC) is an emerging trend in quality control within industrial manufacturing. However, current FSDMC research often lacks generalizability due to its focus on specific datasets. Additionally, defect classification heavily relies on contextual information within images, and existing methods fall short of effectively extracting this information. To address these challenges, we propose a general FSDMC framework called MVREC, which offers two primary advantages: (1) MVREC extracts general features for defect instances by incorporating the pre-trained AlphaCLIP model. (2) It utilizes a region-context framework to enhance defect features by leveraging mask region input and multi-view context augmentation. Furthermore, Few-shot Zip-Adapter(-F) classifiers within the model are introduced to cache the visual features of the support set and perform few-shot classification. We also introduce MVTec-FS, a new FSDMC benchmark based on MVTec AD, which includes 1228 defect images with instance-level mask annotations and 46 defect types. Extensive experiments conducted on MVTec-FS and four additional datasets demonstrate its effectiveness in general defect classification and its ability to incorporate contextual information to improve classification performance. Code: https://github.com/ShuaiLYU/MVREC
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