activity
20172023
most citedHeterogeneous Domain Generalization via Domain Mixup

120 citations · 357 across the 17 of their papers we have counts for

collaborators

17 papers

cs.SD2023

Does My Dog ''Speak'' Like Me? The Acoustic Correlation between Pet Dogs and Their Human Owners

Jieyi Huang, Chunhao Zhang, Yufei Wang +2

How hosts language influence their pets' vocalization is an interesting yet underexplored problem. This paper presents a preliminary investigation into the possible correlation bet…

cs.CV2021★ 2 cited

Embracing the Dark Knowledge: Domain Generalization Using Regularized Knowledge Distillation

Yufei Wang, Haoliang Li, Lap-pui Chau +1

Though convolutional neural networks are widely used in different tasks, lack of generalization capability in the absence of sufficient and representative data is one of the challe…

cs.RO2021★ 15 cited

Learning Visible Connectivity Dynamics for Cloth Smoothing

Xingyu Lin, Yufei Wang, Zixuan Huang +1

Robotic manipulation of cloth remains challenging for robotics due to the complex dynamics of the cloth, lack of a low-dimensional state representation, and self-occlusions. In con…

cs.LG2021★ 1 cited

Privacy-Preserving Constrained Domain Generalization via Gradient Alignment

Chris Xing Tian, Haoliang Li, Yufei Wang +1

Deep neural networks (DNN) have demonstrated unprecedented success for medical imaging applications. However, due to the issue of limited dataset availability and the strict legal…

cs.LG2020★ 4 cited

f-IRL: Inverse Reinforcement Learning via State Marginal Matching

Tianwei Ni, Harshit Sikchi, Yufei Wang +3

Imitation learning is well-suited for robotic tasks where it is difficult to directly program the behavior or specify a cost for optimal control. In this work, we propose a method…

cs.LG2020★ 8 cited

ROLL: Visual Self-Supervised Reinforcement Learning with Object Reasoning

Yufei Wang, Gautham Narayan Narasimhan, Xingyu Lin +2

Current image-based reinforcement learning (RL) algorithms typically operate on the whole image without performing object-level reasoning. This leads to inefficient goal sampling a…