11 papers
Is Task-Specific Training Necessary for Anomaly Detection?
Xingwu Zhang, Guanxuan Li, Paul Henderson +2
Current state-of-the-art multi-class unsupervised anomaly detection (MUAD) methods rely on training encoder--decoder models to reconstruct anomaly-free features. However, we argue…
Masked Generative Policy for Robotic Control
Lipeng Zhuang, Shiyu Fan, Florent P. Audonnet +4
We present Masked Generative Policy (MGP), a novel framework for visuomotor imitation learning. We represent actions as discrete tokens, and train a conditional masked transformer…
3D-ADAM: A Dataset for 3D Anomaly Detection in Additive Manufacturing
Paul McHard, Florent P. Audonnet, Oliver Summerell +3
Surface defects are a primary source of yield loss in manufacturing, yet existing anomaly detection methods often fail in real-world deployment due to limited and unrepresentative…
Benchmarking machine learning models for predicting aerofoil performance
Oliver Summerell, Gerardo Aragon-Camarasa, Stephanie Ordonez Sanchez
This paper investigates the capability of Neural Networks (NNs) as alternatives to the traditional methods to analyse the performance of aerofoils used in the wind and tidal energy…
Diffusion Augmented Retrieval: A Training-Free Approach to Interactive Text-to-Image Retrieval
Zijun Long, Kangheng Liang, Gerardo Aragon-Camarasa +2
Interactive Text-to-image retrieval (I-TIR) is an important enabler for a wide range of state-of-the-art services in domains such as e-commerce and education. However, current meth…
Curio: A Cost-Effective Solution for Robotics Education
Talha Enes Ayranci, Florent P. Audonnet, Gerardo Aragon-Camarasa +2
Student engagement is one of the key challenges in robotics and artificial intelligence (AI) education. Tangible learning approaches, such as educational robots, provide an effecti…