4 citations · 8 across the 9 of their papers we have counts for
11 papers
Masked Generative Motion Planning with Geometry-Guided Token Search
Lipeng Zhuang, Yingdong Ru, Shiyu Fan +3
Generative motion planners typically use learned trajectory priors for initial generation, while leaving test-time repair to local continuous refinement. We introduce Masked Genera…
FoldBack: Self-Correcting Masked Generative Policy for Long-Horizon Garment Folding
Lipeng Zhuang, Shiyu Fan, Yingdong Ru +4
We present FoldBack, a self-correcting masked generative policy for long-horizon garment folding. Existing long-trajectory policies may continue after a missed or slipped grasp eve…
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…
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…
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…
Flat'n'Fold: A Diverse Multi-Modal Dataset for Garment Perception and Manipulation
Lipeng Zhuang, Shiyu Fan, Yingdong Ru +3
We present Flat'n'Fold, a novel large-scale dataset for garment manipulation that addresses critical gaps in existing datasets. Comprising 1,212 human and 887 robot demonstrations…
Understanding and Mitigating Human-Labelling Errors in Supervised Contrastive Learning
Zijun Long, Lipeng Zhuang, George Killick +3
Human-annotated vision datasets inevitably contain a fraction of human mislabelled examples. While the detrimental effects of such mislabelling on supervised learning are well-rese…
Foveation in the Era of Deep Learning
George Killick, Paul Henderson, Paul Siebert +1
In this paper, we tackle the challenge of actively attending to visual scenes using a foveated sensor. We introduce an end-to-end differentiable foveated active vision architecture…
Elucidating and Overcoming the Challenges of Label Noise in Supervised Contrastive Learning
Zijun Long, George Killick, Lipeng Zhuang +3
Image classification datasets exhibit a non-negligible fraction of mislabeled examples, often due to human error when one class superficially resembles another. This issue poses ch…