  Industrial anomaly detection is generally addressed as an unsupervised task
that aims at locating defects with only normal training samples. Recently,
numerous 2D anomaly detection methods have been proposed and have achieved
promising results, however, using only the 2D RGB data as input is not
sufficient to identify imperceptible geometric surface anomalies. Hence, in
this work, we focus on multi-modal anomaly detection. Specifically, we
investigate early multi-modal approaches that attempted to utilize models
pre-trained on large-scale visual datasets, i.e., ImageNet, to construct
feature databases. And we empirically find that directly using these
pre-trained models is not optimal, it can either fail to detect subtle defects
or mistake abnormal features as normal ones. This may be attributed to the
domain gap between target industrial data and source data.Towards this problem,
we propose a Local-to-global Self-supervised Feature Adaptation (LSFA) method
to finetune the adaptors and learn task-oriented representation toward anomaly
detection.Both intra-modal adaptation and cross-modal alignment are optimized
from a local-to-global perspective in LSFA to ensure the representation quality
and consistency in the inference stage.Extensive experiments demonstrate that
our method not only brings a significant performance boost to feature embedding
based approaches, but also outperforms previous State-of-The-Art (SoTA) methods
prominently on both MVTec-3D AD and Eyecandies datasets, e.g., LSFA achieves
97.1% I-AUROC on MVTec-3D, surpass previous SoTA by +3.4%.
