  Visual anomaly classification and segmentation are vital for automating
industrial quality inspection. The focus of prior research in the field has
been on training custom models for each quality inspection task, which requires
task-specific images and annotation. In this paper we move away from this
regime, addressing zero-shot and few-normal-shot anomaly classification and
segmentation. Recently CLIP, a vision-language model, has shown revolutionary
generality with competitive zero-/few-shot performance in comparison to
full-supervision. But CLIP falls short on anomaly classification and
segmentation tasks. Hence, we propose window-based CLIP (WinCLIP) with (1) a
compositional ensemble on state words and prompt templates and (2) efficient
extraction and aggregation of window/patch/image-level features aligned with
text. We also propose its few-normal-shot extension WinCLIP+, which uses
complementary information from normal images. In MVTec-AD (and VisA), without
further tuning, WinCLIP achieves 91.8%/85.1% (78.1%/79.6%) AUROC in zero-shot
anomaly classification and segmentation while WinCLIP+ does 93.1%/95.2%
(83.8%/96.4%) in 1-normal-shot, surpassing state-of-the-art by large margins.
