Therefore, in this work, we tackle anomaly detection in medical images training our framework using only healthy samples. We propose to use the Masked Autoencoder model to learn the structure of the normal samp
In the era of observability, massive amounts of time series data have been collected to monitor the running status of the target system, where anomaly detection serves to identify observations that differ significantly from the remaining ones and is of utmost importance to enable value extraction fr...
Context Autoencoder for Self-Supervised Representation Learning Contextual Representation Learning beyond Masked Language Modeling ContrastMask: Contrastive Learning to Segment Every Thing ConvMAE: Masked Convolution Meets Masked Autoencoders Exploring Plain Vision Transformer Backbones for Object Detection Global...
In the context of anomaly detection, the Convolutional Autoencoder (CAE) is particularly intriguing as it captures the 2D structure within image sequences during the learning phase. A study employed a CAE for anomaly detection, utilizing the reconstruction error of each frame as an anomaly score....
MARLIN: Masked Autoencoder for facial video Representation LearnINg Zhixi Cai1, Shreya Ghosh1,2, Kalin Stefanov1, Abhinav Dhall1,3, Jianfei Cai1, Hamid Rezatofighi1, Reza Haffari1, Munawar Hayat1 1Monash University, 2 Curtin University, 3 Indian Institute of Techn...
Inspired by a modern self-supervised vision transformer model trained using partial image inputs to reconstruct missing image regions -- we propose AMAE, a two-stage algorithm for adaptation of the pre-trained masked autoencoder (MAE). Starting from MAE initialization, AMAE first creates synthetic ...
spatiotemporal masked autoencodervision transformerskip connectionsVideo anomaly detection is a critical component of intelligent video surveillance systems, extensively deployed and researched in industry and academia. However, existing methods have a strong generalization ability for predicting anomaly samples....
In this work, we extend MAE to perform anomaly detection on breast magnetic resonance imaging (MRI). This new model, coined masked autoencoder for medical imaging (MAEMI) is trained on two non-contrast enhanced MRI sequences, aiming at lesion detection without the need for intravenous injection...
Semi-supervised noise-resilient anomaly detection with feature autoencoder 2024, Knowledge-Based Systems Citation Excerpt : Anomaly detection in industrial images is an important subset of this field. These methods are mainly divided into two categories: feature embedding [6,24,25] and reconstruction-...
We introduce MEGA, a MaskEd Generative Autoencoder trained to recover human meshes from images and partial human mesh token sequences. Given an image, our flexible generation scheme allows us to predict a single human mesh in deterministic mode or to generate multiple human meshes in stochastic ...