TextureCLIP: Cross-Dataset Zero-Shot Texture Anomaly Segmentation with Triadic Descriptive Prompting

Texture anomaly segmentation aims to localize irregularities on textured surfaces, a task critical for industrial quality control. Supervised methods require extensive labeled data, while unsupervised approaches often struggle to generalize to unseen target domains. Recent zero-shot methods based on vision-language models such as Contrastive Language-Image Pretraining (CLIP) enable anomaly detection through text prompts without target-domain training data. However, existing approaches typically rely on generic prompts and show limited sensitivity to fine-grained texture variations. To address these limitations, we propose TextureCLIP, a cross-dataset zero-shot framework with auxiliary training for texture anomaly segmentation. The framework is trained on source texture data from the MVTec AD texture subset using annotated source-domain samples and directly evaluated on six unseen target datasets without access to target-domain training images, annotations, or fine-tuning. The proposed Triadic Descriptive Prompting (TriDP) integrates normal prompts, generic anomaly prompts, and descriptive anomaly prompts to provide complementary semantic cues for improved cross-domain generalization. To enhance spatial sensitivity, Dual Attention Modules (DAMs) are incorporated into the CLIP image encoder to refine local feature representations. In addition, Softmax-Weighted Averaging (SMWA) aggregates multiple anomaly cues by emphasizing the prompt responses with higher similarity scores. Experimental results demonstrate that TextureCLIP achieves strong and consistent performance across diverse texture datasets, attaining 67.06% AP and 65.69% F1-max, with improvements of 5.17 and 2.66 percentage points over the competitive baselines, respectively.