[2603.19608] FB-CLIP: Fine-Grained Zero-Shot Anomaly Detection with Foreground-Background Disentanglement

Computer Science > Computer Vision and Pattern Recognition arXiv:2603.19608 (cs) [Submitted on 20 Mar 2026] Title:FB-CLIP: Fine-Grained Zero-Shot Anomaly Detection with Foreground-Background Disentanglement Authors:Ming Hu, Yongsheng Huo, Mingyu Dou, Jianfu Yin, Peng Zhao, Yao Wang, Cong Hu, Bingliang Hu, Quan Wang View a PDF of the paper titled FB-CLIP: Fine-Grained Zero-Shot Anomaly Detection with Foreground-Background Disentanglement, by Ming Hu and 8 other authors View PDF HTML (experimental) Abstract:Fine-grained anomaly detection is crucial in industrial and medical applications, but labeled anomalies are often scarce, making zero-shot detection challenging. While vision-language models like CLIP offer promising solutions, they struggle with foreground-background feature entanglement and coarse textual semantics. We propose FB-CLIP, a framework that enhances anomaly localization via multi-strategy textual representations and foreground-background separation. In the textual modality, it combines End-of-Text features, global-pooled representations, and attention-weighted token features for richer semantic cues. In the visual modality, multi-view soft separation along identity, semantic, and spatial dimensions, together with background suppression, reduces interference and improves discriminability. Semantic Consistency Regularization (SCR) aligns image features with normal and abnormal textual prototypes, suppressing uncertain matches and enlarging semantic gaps. Exper...