CostFilter-AD: Enhancing Anomaly Detection through Matching Cost Filtering
Journal:
arXiv
Published Date:
May 2, 2025
Abstract
Unsupervised anomaly detection (UAD) seeks to localize the anomaly mask of an
input image with respect to normal samples. Either by reconstructing normal
counterparts (reconstruction-based) or by learning an image feature embedding
space (embedding-based), existing approaches fundamentally rely on image-level
or feature-level matching to derive anomaly scores. Often, such a matching
process is inaccurate yet overlooked, leading to sub-optimal detection. To
address this issue, we introduce the concept of cost filtering, borrowed from
classical matching tasks, such as depth and flow estimation, into the UAD
problem. We call this approach {\em CostFilter-AD}. Specifically, we first
construct a matching cost volume between the input and normal samples,
comprising two spatial dimensions and one matching dimension that encodes
potential matches. To refine this, we propose a cost volume filtering network,
guided by the input observation as an attention query across multiple feature
layers, which effectively suppresses matching noise while preserving edge
structures and capturing subtle anomalies. Designed as a generic
post-processing plug-in, CostFilter-AD can be integrated with either
reconstruction-based or embedding-based methods. Extensive experiments on
MVTec-AD and VisA benchmarks validate the generic benefits of CostFilter-AD for
both single- and multi-class UAD tasks. Code and models will be released at
https://github.com/ZHE-SAPI/CostFilter-AD.
