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。