In this technical report, we briefly introduce our solution for the
Zero/Few-shot Track of the Visual Anomaly and Novelty Detection (VAND) 2023
Challenge. For industrial visual inspection, building a single model that can
be rapidly adapted to numerous categories without or with only a few normal
reference images is a promising research direction. This is primarily because
of the vast variety of the product types. For the zero-shot track, we propose a
solution based on the CLIP model by adding extra linear layers. These layers
are used to map the image features to the joint embedding space, so that they
can compare with the text features to generate the anomaly maps. Besides, when
the reference images are available, we utilize multiple memory banks to store
their features and compare them with the features of the test images during the
testing phase. In this challenge, our method achieved first place in the
zero-shot track, especially excelling in segmentation with an impressive F1
score improvement of 0.0489 over the second-ranked participant. Furthermore, in
the few-shot track, we secured the fourth position overall, with our
classification F1 score of 0.8687 ranking first among all participating teams.

本文介绍了用于工业视觉检测的一个方案，该方案使用 CLIP 模型和多个存储器来实现无标准参考图像的零射和少射跟踪，从而实现对大量产品类型的快速自适应，并在 VAND 2023 挑战赛中获得了一等奖。

CVPR 2023 VAND 工作坊挑战赛 1 和 2 的零 / 少样本异常分类和分割方法：在零样本 AD 上获得第一名，在少样本 AD 上获得第四名

A Zero-/Few-Shot Anomaly Classification and Segmentation Method for CVPR  2023 VAND Workshop Challenge Tracks 1&2: 1st Place on Zero-shot AD and 4th  Place on Few-shot AD

Industrial visual inspection aims at detecting surface defects in products
during the manufacturing process. Although existing anomaly detection models
have shown great performance on many public benchmarks, their limited
adjustability and ability to detect logical anomalies hinder their broader use
in real-world settings. To this end, in this paper, we propose a novel
component-aware anomaly detection framework (ComAD) which can simultaneously
achieve adjustable and logical anomaly detection for industrial scenarios.
Specifically, we propose to segment images into multiple components based on a
lightweight and nearly training-free unsupervised semantic segmentation model.
Then, we design an interpretable logical anomaly detection model through
modeling the metrological features of each component and their relationships.
Despite its simplicity, our framework achieves state-of-the-art performance on
image-level logical anomaly detection. Meanwhile, segmenting a product image
into multiple components provides a novel perspective for industrial visual
inspection, demonstrating great potential in model customization, noise
resistance, and anomaly classification. The code will be available at
this https URL

本文介绍了一种新的组件感知异常检测框架 (ComAD)，该框架利用轻量级和几乎不需要训练的无监督语义分割模型将图像分割成多个组件，并通过建模每个组件和它们之间的关系来实现可调节的和逻辑异常检测，该框架在图像级逻辑异常检测方面取得了最新的性能，并且将产品图像分成多个组件提供了一个新的视角，具有在模型定制、噪声抵抗和异常分类方面巨大的潜力。