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.

翻译：本技术报告概述了我们在2023年视觉异常与新颖性检测（VAND）挑战赛零样本赛道中的提交方案。基于WINCLIP框架的性能，我们旨在通过集成零样本分割模型来增强系统的定位能力。此外，我们执行前景实例分割，使模型能够聚焦于图像的相关区域，从而更好地识别微小或细微的偏差。我们的流水线无需外部数据或信息，可直接应用于新数据集。本团队（Variance Vigilance Vanguard）在VAND挑战赛零样本赛道中排名第三，并在VisA数据集上实现了样本级/像素级平均F1最高分分别为81.5/24.2。