VarAD: Lightweight High-Resolution Image Anomaly Detection via Visual Autoregressive Modeling
This addresses the practical problem of efficient anomaly detection in high-resolution images for industrial inspection applications, representing an incremental improvement with a novel method for a known bottleneck.
The paper tackles high-resolution image anomaly detection (HRIAD) by proposing VarAD, a method based on visual autoregressive modeling for token prediction, which achieves superior performance on four public datasets and a real-world button inspection dataset while maintaining lightweight computational requirements.
This paper addresses a practical task: High-Resolution Image Anomaly Detection (HRIAD). In comparison to conventional image anomaly detection for low-resolution images, HRIAD imposes a heavier computational burden and necessitates superior global information capture capacity. To tackle HRIAD, this paper translates image anomaly detection into visual token prediction and proposes VarAD based on visual autoregressive modeling for token prediction. Specifically, VarAD first extracts multi-hierarchy and multi-directional visual token sequences, and then employs an advanced model, Mamba, for visual autoregressive modeling and token prediction. During the prediction process, VarAD effectively exploits information from all preceding tokens to predict the target token. Finally, the discrepancies between predicted tokens and original tokens are utilized to score anomalies. Comprehensive experiments on four publicly available datasets and a real-world button inspection dataset demonstrate that the proposed VarAD achieves superior high-resolution image anomaly detection performance while maintaining lightweight, rendering VarAD a viable solution for HRIAD. Code is available at \href{https://github.com/caoyunkang/VarAD}{\url{https://github.com/caoyunkang/VarAD}}.