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cs.CV2025
Towards Efficient Pixel Labeling for Industrial Anomaly Detection and Localization
Jingqi Wu, Hanxi Li, Lin Yuanbo Wu +3
Industrial product inspection is often performed using Anomaly Detection (AD) frameworks trained solely on non-defective samples. Although defective samples can be collected during…
cs.CV2025
A Novel Local Focusing Mechanism for Deepfake Detection Generalization
Mingliang Li, Lin Yuanbo Wu, Changhong Liu +1
The rapid advancement of deepfake generation techniques has intensified the need for robust and generalizable detection methods. Existing approaches based on reconstruction learnin…
cs.CV2025
Self-Navigated Residual Mamba for Universal Industrial Anomaly Detection
Hanxi Li, Jingqi Wu, Lin Yuanbo Wu +4
In this paper, we propose Self-Navigated Residual Mamba (SNARM), a novel framework for universal industrial anomaly detection that leverages ``self-referential learning'' within te…