Contrastive Language-Image Pre-training (CLIP) models have shown promising
performance on zero-shot visual recognition tasks by learning visual
representations under natural language supervision. Recent studies attempt the
use of CLIP to tackle zero-shot anomaly detection by matching images with
normal and abnormal state prompts. However, since CLIP focuses on building
correspondence between paired text prompts and global image-level
representations, the lack of patch-level vision to text alignment limits its
capability on precise visual anomaly localization. In this work, we introduce a
training-free adaptation (TFA) framework of CLIP for zero-shot anomaly
localization. In the visual encoder, we innovate a training-free value-wise
attention mechanism to extract intrinsic local tokens of CLIP for patch-level
local description. From the perspective of text supervision, we particularly
design a unified domain-aware contrastive state prompting template. On top of
the proposed TFA, we further introduce a test-time adaptation (TTA) mechanism
to refine anomaly localization results, where a layer of trainable parameters
in the adapter is optimized using TFA's pseudo-labels and synthetic
noise-corrupted tokens. With both TFA and TTA adaptation, we significantly
exploit the potential of CLIP for zero-shot anomaly localization and
demonstrate the effectiveness of our proposed methods on various datasets.

使用 Contrastive Language-Image Pre-training (CLIP) 模型进行零样本异常检测，通过学习自然语言监督下的视觉表示，构建文本提示与全局图像级别表示之间的对应关系，采用训练自由的价值关注机制提取 CLIP 的内在局部标记以进行精确定位，设计一个统一的领域感知对比状态提示模板，通过测试时的自适应机制进一步优化异常定位结果。