Long-Tailed Anomaly Detection with Learnable Class Names
Chih-Hui Ho Kuan-Chuan Peng Nuno Vasconcelos
Anomaly detection (AD) aims to identify defective images and localize their
defects (if any). Ideally, AD models should be able to detect defects over many
image classes; without relying on hard-coded class names that can be
uninformative or inconsistent across datasets; learn without anomaly
supervision; and be robust to the long-tailed distributions of real-world
applications. To address these challenges, we formulate the problem of
long-tailed AD by introducing several datasets with different levels of class
imbalance and metrics for performance evaluation. We then propose a novel
method, LTAD, to detect defects from multiple and long-tailed classes, without
relying on dataset class names. LTAD combines AD by reconstruction and semantic
AD modules. AD by reconstruction is implemented with a transformer-based
reconstruction module. Semantic AD is implemented with a binary classifier,
which relies on learned pseudo class names and a pretrained foundation model.
These modules are learned over two phases. Phase 1 learns the pseudo-class
names and a variational autoencoder (VAE) for feature synthesis that augments
the training data to combat long-tails. Phase 2 then learns the parameters of
the reconstruction and classification modules of LTAD. Extensive experiments
using the proposed long-tailed datasets show that LTAD substantially
outperforms the state-of-the-art methods for most forms of dataset imbalance.
The long-tailed dataset split is available at
https://zenodo.org/records/10854201 .