  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.
