Search Results for author: Zhuliang Yu

Found 5 papers, 1 papers with code

Disentangling Writer and Character Styles for Handwriting Generation

1 code implementation CVPR 2023 Gang Dai, Yifan Zhang, Qingfeng Wang, Qing Du, Zhuliang Yu, Zhuoman Liu, Shuangping Huang

In light of this, we propose to disentangle the style representations at both writer and character levels from individual handwritings to synthesize realistic stylized online handwritten characters.

Handwriting generation

Cross-Correlation Based Discriminant Criterion for Channel Selection in Motor Imagery BCI Systems

no code implementations3 Dec 2020 Jianli Yu, Zhuliang Yu

In this article, we proposed a cross-correlation based discriminant criterion (XCDC) which assesses the importance of a channel for discriminating the mental states of different motor imagery (MI) tasks.

EEG Motor Imagery

Joint Representation of Multiple Geometric Priors via a Shape Decomposition Model for Single Monocular 3D Pose Estimation

no code implementations31 May 2019 Mengxi Jiang, Zhuliang Yu, Cuihua Li, Yunqi Lei

Specifically, we propose a Shape Decomposition Model (SDM) in which a 3D pose is considered as the superposition of two parts which are global structure together with some deformations.

3D Pose Estimation Dictionary Learning

You Only Look & Listen Once: Towards Fast and Accurate Visual Grounding

no code implementations12 Feb 2019 Chaorui Deng, Qi Wu, Guanghui Xu, Zhuliang Yu, Yanwu Xu, Kui Jia, Mingkui Tan

Most state-of-the-art methods in VG operate in a two-stage manner, wherein the first stage an object detector is adopted to generate a set of object proposals from the input image and the second stage is simply formulated as a cross-modal matching problem that finds the best match between the language query and all region proposals.

object-detection Object Detection +2

An Iterative Boundary Random Walks Algorithm for Interactive Image Segmentation

no code implementations9 Aug 2018 Xiao-Feng Xie, Zhuliang Yu, Zhenghui Gu, Yuanqing Li

Two approaches, iterative random walks and boundary random walks, are proposed for segmentation potential, which is the key step in feedback system.

Image Segmentation Segmentation +2

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