5 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…
Eliminating Hallucination in Diffusion-Augmented Interactive Text-to-Image Retrieval
Zhuocheng Zhang, Kangheng Liang, Guanxuan Li +3
Diffusion-Augmented Interactive Text-to-Image Retrieval (DAI-TIR) is a promising paradigm that improves retrieval performance by generating query images via diffusion models and us…
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…
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…