activity
20162022
most citedWeakly Supervised Semantic Segmentation for Large-Scale Point Cloud

8 citations · 11 across the 3 of their papers we have counts for

collaborators

6 papers

cs.CV20228 cited

Weakly Supervised Semantic Segmentation for Large-Scale Point Cloud

Yachao Zhang, Zonghao Li, Yuan Xie +3

Existing methods for large-scale point cloud semantic segmentation require expensive, tedious and error-prone manual point-wise annotations. Intuitively, weakly supervised training…

cs.CV20203 cited

NTIRE 2020 Challenge on Image Demoireing: Methods and Results

Shanxin Yuan, Radu Timofte, Ales Leonardis +43

This paper reviews the Challenge on Image Demoireing that was part of the New Trends in Image Restoration and Enhancement (NTIRE) workshop, held in conjunction with CVPR 2020. Demo…

cs.CV2019

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

Mengxi Jiang, Zhuliang Yu, Cuihua Li +1

In this paper, we aim to recover the 3D human pose from 2D body joints of a single image. The major challenge in this task is the depth ambiguity since different 3D poses may produ…

cs.CV2018

Bi-GANs-ST for Perceptual Image Super-resolution

Xiaotong Luo, Rong Chen, Yuan Xie +2

Image quality measurement is a critical problem for image super-resolution (SR) algorithms. Usually, they are evaluated by some well-known objective metrics, e.g., PSNR and SSIM, b…

cs.CV2018

Jointly Deep Multi-View Learning for Clustering Analysis

Bingqian Lin, Yuan Xie, Yanyun Qu +2

In this paper, we propose a novel Joint framework for Deep Multi-view Clustering (DMJC), where multiple deep embedded features, multi-view fusion mechanism and clustering assignmen…

cs.CV2016

An Effective Unconstrained Correlation Filter and Its Kernelization for Face Recognition

Yan Yan, Hanzi Wang, Cuihua Li +2

In this paper, an effective unconstrained correlation filter called Uncon- strained Optimal Origin Tradeoff Filter (UOOTF) is presented and applied to robust face recognition. Comp…