activity
20192022
most citedEDCNN: Edge enhancement-based Densely Connected Network with Compound Loss for Low-Dose CT Denoising

143 citations · 155 across the 7 of their papers we have counts for

collaborators

7 papers

cs.LG20222 cited

GLAN: A Graph-based Linear Assignment Network

He Liu, Tao Wang, Congyan Lang +3

Differentiable solvers for the linear assignment problem (LAP) have attracted much research attention in recent years, which are usually embedded into learning frameworks as compon…

cs.CV20221 cited

Deep Probabilistic Graph Matching

He Liu, Tao Wang, Yidong Li +3

Most previous learning-based graph matching algorithms solve the \textit{quadratic assignment problem} (QAP) by dropping one or more of the matching constraints and adopting a rela…

cs.CV20211 cited

MSO: Multi-Feature Space Joint Optimization Network for RGB-Infrared Person Re-Identification

Yajun Gao, Tengfei Liang, Yi Jin +4

The RGB-infrared cross-modality person re-identification (ReID) task aims to recognize the images of the same identity between the visible modality and the infrared modality. Exist…

cs.CV20211 cited

A Universal Model for Cross Modality Mapping by Relational Reasoning

Zun Li, Congyan Lang, Liqian Liang +4

With the aim of matching a pair of instances from two different modalities, cross modality mapping has attracted growing attention in the computer vision community. Existing method…

cs.CV20214 cited

Attention Models for Point Clouds in Deep Learning: A Survey

Xu Wang, Yi Jin, Yigang Cen +2

Recently, the advancement of 3D point clouds in deep learning has attracted intensive research in different application domains such as computer vision and robotic tasks. However,…

eess.IV2020143 cited

EDCNN: Edge enhancement-based Densely Connected Network with Compound Loss for Low-Dose CT Denoising

Tengfei Liang, Yi Jin, Yidong Li +3

In the past few decades, to reduce the risk of X-ray in computed tomography (CT), low-dose CT image denoising has attracted extensive attention from researchers, which has become a…