FC2P: Feature Cross-Channel Projection for Unsupervised Anomaly Segmentation
Yichi Chen, Weizhi Xian, Junjie Wang, Xian Tao, Bin Chen · IEEE Transactions on Instrumentation and Measurement · 2025
Unsupervised anomaly segmentation plays a critical role in real-world industrial product quality inspection. While feature reconstruction-based methods have shown promising performance by detecting anomalies through differences between pre-trained features and their reconstructions, existing approaches often suffer from shortcut learning, leading to reconstruction failures and inaccurate anomaly representation across multi-stage features. To address these limitations, we propose Feature Cross-Channel Projection (FC2P), a novel approach for anomaly segmentation. FC2P divides features into two subsets based on neighboring channels, and employs two autoencoders for closed-loop prediction, effectively mitigating shortcut effects while capturing semantic relationships for efficient reconstruction. Additionally, we introduce an Anomaly Exposure Network (AExNet), which progressively amplifies anomalies across multi-stage feature residuals, generating precise anomaly score maps for accurate segmentation. Extensive experiments on MVTec AD and Visa benchmark datasets demonstrate that the proposed FC2P achieves state-of-the-art performance, with average precision (AP) scores of 79.8% and 44.8%, respectively. Moreover, visualization results on real industrial data further show the practicality of our proposed method. The code will be made publicly available at https://github.com/Karma1628/work-2 to ensure reproducibility and facilitate further research.