3 citations · 4 across the 6 of their papers we have counts for
10 papers
Continuous-Time Cloth Dynamics with Physics-Informed Neural ODEs
Yingdong Ru, Lipeng Zhuang, Zhuo He +1
We present ContinuousCloth, a continuous-time, physics-informed neural solver for deformable cloth dynamics that enforces a spring--mass ODE within a neural trajectory model. Unlik…
ClothCode: Generative Modeling for Garment State Estimation and Dynamics Prediction
Yingdong Ru, Lipeng Zhuang, Shiyu Fan +3
We introduce ClothCode, a unified generative framework for garment state estimation and action-conditioned dynamics prediction in a shared discrete configuration space. A material-…
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…
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…
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…
RoboLLM: Robotic Vision Tasks Grounded on Multimodal Large Language Models
Zijun Long, George Killick, Richard McCreadie +1
Robotic vision applications often necessitate a wide range of visual perception tasks, such as object detection, segmentation, and identification. While there have been substantial…
MultiWay-Adapater: Adapting large-scale multi-modal models for scalable image-text retrieval
Zijun Long, George Killick, Richard McCreadie +1
As Multimodal Large Language Models (MLLMs) grow in size, adapting them to specialized tasks becomes increasingly challenging due to high computational and memory demands. Indeed,…
When hard negative sampling meets supervised contrastive learning
Zijun Long, George Killick, Richard McCreadie +2
State-of-the-art image models predominantly follow a two-stage strategy: pre-training on large datasets and fine-tuning with cross-entropy loss. Many studies have shown that using…
LaCViT: A Label-aware Contrastive Fine-tuning Framework for Vision Transformers
Zijun Long, Zaiqiao Meng, Gerardo Aragon Camarasa +1
Vision Transformers (ViTs) have emerged as popular models in computer vision, demonstrating state-of-the-art performance across various tasks. This success typically follows a two-…