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arXiv · 2601.09147

SSVP: Synergistic Semantic-Visual Prompting for Industrial Zero-Shot Anomaly Detection

Chenhao Fu·Han Fang·Xiuzheng Zheng·Wenbo Wei·Yonghua Li·Hao Sun·Xuelong Li
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Abstract

Zero-Shot Anomaly Detection (ZSAD) leverages Vision-Language Models (VLMs) to enable supervision-free industrial inspection. However, existing ZSAD paradigms are constrained by single visual backbones, which struggle to balance global semantic generalization with fine-grained structural discriminability. To bridge this gap, we propose Synergistic Semantic-Visual Prompting (SSVP), that efficiently fuses diverse visual encodings to elevate model's fine-grained perception. Specifically, SSVP introduces the Hierarchical Semantic-Visual Synergy (HSVS) mechanism, which deeply integrates DINOv3's multi-scale structural priors into the CLIP semantic space. Subsequently, the Vision-Conditioned Prompt Generator (VCPG) employs cross-modal attention to guide dynamic prompt generation, enabling linguistic queries to precisely anchor to specific anomaly patterns. Furthermore, to address the discrepancy between global scoring and local evidence, the Visual-Text Anomaly Mapper (VTAM) establishes a dual-gated calibration paradigm. Extensive evaluations on seven industrial benchmarks validate the robustness of our method; SSVP achieves state-of-the-art performance with 93.0% Image-AUROC and 92.2% Pixel-AUROC on MVTec-AD, significantly outperforming existing zero-shot approaches.

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Chenhao Fu, Han Fang, Xiuzheng Zheng, Wenbo Wei, Yonghua Li, Hao Sun, Xuelong Li. 2026-01-19. SSVP: Synergistic Semantic-Visual Prompting for Industrial Zero-Shot Anomaly Detection. https://arxiv.org/abs/2601.09147

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