HaloAE: An HaloNet based Local Transformer Auto-Encoder for Anomaly Detection and Localization

TL;DR

HaloAE introduces a novel local Transformer auto-encoder that combines convolutional and self-attention layers for efficient anomaly detection and localization, achieving competitive results on the MVTec dataset.

Contribution

This work presents the first local 2D Transformer auto-encoder using HaloNet for anomaly detection, integrating local self-attention with convolutional features.

Findings

Competitive results on MVTec dataset

Effective combination of convolution and local self-attention

Demonstrates benefits of local Transformer models for anomaly tasks

Abstract

Unsupervised anomaly detection and localization is a crucial task as it is impossible to collect and label all possible anomalies. Many studies have emphasized the importance of integrating local and global information to achieve accurate segmentation of anomalies. To this end, there has been a growing interest in Transformer, which allows modeling long-range content interactions. However, global interactions through self attention are generally too expensive for most image scales. In this study, we introduce HaloAE, the first auto-encoder based on a local 2D version of Transformer with HaloNet. With HaloAE, we have created a hybrid model that combines convolution and local 2D block-wise self-attention layers and jointly performs anomaly detection and segmentation through a single model. We achieved competitive results on the MVTec dataset, suggesting that vision models incorporating Transformer could benefit from a local computation of the self-attention operation, and pave the way for other applications.