Test Time Training for Industrial Anomaly Segmentation
Alex Costanzino Pierluigi Zama Ramirez Mirko Del Moro Agostino Aiezzo Giuseppe Lisanti Samuele Salti Luigi Di Stefano
Anomaly Detection and Segmentation (AD&S) is crucial for industrial quality
control. While existing methods excel in generating anomaly scores for each
pixel, practical applications require producing a binary segmentation to
identify anomalies. Due to the absence of labeled anomalies in many real
scenarios, standard practices binarize these maps based on some statistics
derived from a validation set containing only nominal samples, resulting in
poor segmentation performance. This paper addresses this problem by proposing a
test time training strategy to improve the segmentation performance. Indeed, at
test time, we can extract rich features directly from anomalous samples to
train a classifier that can discriminate defects effectively. Our general
approach can work downstream to any AD&S method that provides an anomaly score
map as output, even in multimodal settings. We demonstrate the effectiveness of
our approach over baselines through extensive experimentation and evaluation on
MVTec AD and MVTec 3D-AD.