Masked Transformer for image Anomaly Localization
arXiv:2210.15540 · doi:10.1142/S0129065722500307
Abstract
Image anomaly detection consists in detecting images or image portions that are visually different from the majority of the samples in a dataset. The task is of practical importance for various real-life applications like biomedical image analysis, visual inspection in industrial production, banking, traffic management, etc. Most of the current deep learning approaches rely on image reconstruction: the input image is projected in some latent space and then reconstructed, assuming that the network (mostly trained on normal data) will not be able to reconstruct the anomalous portions. However, this assumption does not always hold. We thus propose a new model based on the Vision Transformer architecture with patch masking: the input image is split in several patches, and each patch is reconstructed only from the surrounding data, thus ignoring the potentially anomalous information contained in the patch itself. We then show that multi-resolution patches and their collective embeddings provide a large improvement in the model's performance compared to the exclusive use of the traditional square patches. The proposed model has been tested on popular anomaly detection datasets such as MVTec and head CT and achieved good results when compared to other state-of-the-art approaches.
References in corpus (8)
- An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
- Generative Adversarial Networks
- Uninformed Students: Student-Teacher Anomaly Detection with Discriminative Latent Embeddings
- Efficient GAN-Based Anomaly Detection
- Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders
- Deep Autoencoding Models for Unsupervised Anomaly Segmentation in Brain MR Images
- Deep Anomaly Detection Using Geometric Transformations
- The MVTec 3D-AD Dataset for Unsupervised 3D Anomaly Detection and Localization