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

143 citations · 153 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CV2022

2D+3D facial expression recognition via embedded tensor manifold regularization

Yunfang Fu, Qiuqi Ruan, Ziyan Luo +3

In this paper, a novel approach via embedded tensor manifold regularization for 2D+3D facial expression recognition (FERETMR) is proposed. Firstly, 3D tensors are constructed from…

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.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.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…

cs.LG20193 cited

HERA: Partial Label Learning by Combining Heterogeneous Loss with Sparse and Low-Rank Regularization

Gengyu Lyu, Songhe Feng, Yi Jin +3

Partial Label Learning (PLL) aims to learn from the data where each training instance is associated with a set of candidate labels, among which only one is correct. Most existing m…