Abstract
Multi-class anomaly detection has been a promising research area. However, most methods focus on increasing backbone parameters or the depth of the network. This study uses multi-texture anomaly detection as an example to validate a lightweight flow-based pipeline called Multi-Scale Siamese Flow (MSSF) with a Multi-level Feature Fusion (MLFF) to fully use extracted shallow and deep features. Besides, a Mixed anomalies synthesis (MAS) method is incorporated into the MSSF and trains our pipeline in a self-supervised manner by designing a novel training loss combining negative log-likelihood with a changeable self-supervised hindering loss. Extensive experiments on real-world texture subsets or texture datasets, including MVTec-AD, KSDD2, MT, and AITEX, indicate the effectiveness of our MSSF. The inference speed surpasses the second fastest method, UniAD, about 2 times. Compared with other cutting-edge methods, the MSSF achieves an effective balance between performance and speed.
J. Zhang—This work was supported by Robotics Institute of Zhejiang University under Grant K12201.
Y. Chen and Z. Hu—Equal first authorship.
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Chen, Y., Hu, Z., Huang, L., Zhang, J. (2025). MSSF: A Multi-scale Siamese Flow Architecture for Multi-texture Class Anomaly Detection. In: Antonacopoulos, A., Chaudhuri, S., Chellappa, R., Liu, CL., Bhattacharya, S., Pal, U. (eds) Pattern Recognition. ICPR 2024. Lecture Notes in Computer Science, vol 15317. Springer, Cham. https://doi.org/10.1007/978-3-031-78447-7_3
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