An Outlier Exposure Approach to Improve Visual Anomaly Detection Performance for Mobile Robots
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Robotics and Automation Letters"
DOI: 10.1109/lra.2022.3192794
Abstract: We consider the problem of building visual anomaly detection systems for mobile robots. Standard anomaly detection models are trained using large datasets composed only of non-anomalous data. However, in robotics applications, it is often the… read more here.
Keywords: outlier exposure; mobile robots; visual anomaly; detection ... See more keywords
Dual-Attention Transformer and Discriminative Flow for Industrial Visual Anomaly Detection
Sign Up to like & getrecommendations! Published in 2024 at "IEEE Transactions on Automation Science and Engineering"
DOI: 10.1109/tase.2023.3322156
Abstract: In this paper, we introduce the novel state-of-the-art Dual-attention Transformer and Discriminative Flow (DADF) framework for visual anomaly detection. Based on only normal knowledge, visual anomaly detection has wide applications in industrial scenarios and has… read more here.
Keywords: detection; attention; anomaly detection; visual anomaly ... See more keywords
Self-Supervision-Augmented Deep Autoencoder for Unsupervised Visual Anomaly Detection.
Sign Up to like & getrecommendations! Published in 2021 at "IEEE transactions on cybernetics"
DOI: 10.1109/tcyb.2021.3127716
Abstract: Deep autoencoder (AE) has demonstrated promising performances in visual anomaly detection (VAD). Learning normal patterns on normal data, deep AE is expected to yield larger reconstruction errors for anomalous samples, which is utilized as the… read more here.
Keywords: augmented deep; visual anomaly; self supervision; anomaly detection ... See more keywords