This technical report outlines our submission to the zero-shot track of the
Visual Anomaly and Novelty Detection (VAND) 2023 Challenge. Building on the
performance of the WINCLIP framework, we aim to enhance the system's
localization capabilities by integrating zero-shot segmentation models. In
addition, we perform foreground instance segmentation which enables the model
to focus on the relevant parts of the image, thus allowing the models to better
identify small or subtle deviations. Our pipeline requires no external data or
information, allowing for it to be directly applied to new datasets. Our team
(Variance Vigilance Vanguard) ranked third in the zero-shot track of the VAND
challenge, and achieve an average F1-max score of 81.5/24.2 at a sample/pixel
level on the VisA dataset.

本文介绍了我们参加 Visual Anomaly and Novelty Detection (VAND) 2023 Challenge 中的零样本检测方向的提交结果。我们在 WINCLIP 框架的基础上增强了系统的定位能力，同时整合了零样本分割模型。此外，我们还进行了前景实例分割，使模型集中在图像的相关部分，从而更好地识别小型或细微的偏差。我们的管道不需要外部数据或信息，可以直接应用于新的数据集。我们的团队 (Variance Vigilance Vanguard) 在 VAND 挑战赛的零样本检测中排名第三，样本 / 像素级别上在 VisA 数据集上实现了平均 F1-max 得分为 81.5/24.2。

使用预训练分割模型进行零样本异常检测

Zero-Shot Anomaly Detection with Pre-trained Segmentation Models

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