Attention Guided Anomaly Detection and Localization in Images

Anomaly detection and localization is a popular computer vision problem involving detecting anomalous images and localizing anomalies within them. However, this task is challenging due to the small sample size and pixel coverage of the anomaly in real-world scenarios. Prior works need to use anomalous training images to compute a threshold to detect and localize anomalies. To remove this need, we propose Convolutional Adversarial Variational autoencoder with Guided Attention (CAVGA), which localizes the anomaly with a convolutional latent variable to preserve the spatial information. In the unsupervised setting, we propose an attention expansion loss, where we encourage CAVGA to focus on all normal regions in the image without using any anomalous training image. Furthermore, using only 2 anomalous images in the weakly supervised setting we propose a complementary guided attention loss, where we encourage the normal attention to focus on all normal regions while minimizing the regions covered by the anomalous attention in the normal image. CAVGA outperforms the state-of-the-art (SOTA) anomaly detection methods on the MNIST, CIFAR-10, Fashion-MNIST, MVTec Anomaly Detection (MVTAD), and modified ShanghaiTech Campus (mSTC) datasets. CAVGA also outperforms the SOTA anomaly localization methods on the MVTAD and mSTC datasets.


page 1

page 3

page 5

page 6

page 7

page 8


Attention Guided Anomaly Localization in Images

Anomaly localization is an important problem in computer vision which in...

DRÆM – A discriminatively trained reconstruction embedding for surface anomaly detection

Visual surface anomaly detection aims to detect local image regions that...

Self-Supervised Masking for Unsupervised Anomaly Detection and Localization

Recently, anomaly detection and localization in multimedia data have rec...

Multiresolution Knowledge Distillation for Anomaly Detection

Unsupervised representation learning has proved to be a critical compone...

EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies

Detecting anomalies in images is an important task, especially in real-t...

Lossy Compression for Robust Unsupervised Time-Series Anomaly Detection

A new Lossy Causal Temporal Convolutional Neural Network Autoencoder for...

DFR: Deep Feature Reconstruction for Unsupervised Anomaly Segmentation

Automatic detecting anomalous regions in images of objects or textures w...

Please sign up or login with your details

Forgot password? Click here to reset