activity
20172020
most citedClassification of Hyperspectral and LiDAR Data Using Coupled CNNs

464 citations · 541 across the 11 of their papers we have counts for

collaborators

14 papers

eess.IV20205 cited

Multiscale Point Cloud Geometry Compression

Jianqiang Wang, Dandan Ding, Zhu Li +1

Recent years have witnessed the growth of point cloud based applications because of its realistic and fine-grained representation of 3D objects and scenes. However, it is a challen…

cs.CV2020

Dense-View GEIs Set: View Space Covering for Gait Recognition based on Dense-View GAN

Rijun Liao, Weizhi An, Shiqi Yu +2

Gait recognition has proven to be effective for long-distance human recognition. But view variance of gait features would change human appearance greatly and reduce its performance…

eess.IV20202 cited

Referenceless Rate-Distortion Modeling with Learning from Bitstream and Pixel Features

Yangfan Sun, Li Li, Zhu Li +1

Generally, adaptive bitrates for variable Internet bandwidths can be obtained through multi-pass coding. Referenceless prediction-based methods show practical benefits compared wit…

eess.IV2020

Inferring Point Cloud Quality via Graph Similarity

Qi Yang, Zhan Ma, Yiling Xu +2

We propose the GraphSIM -- an objective metric to accurately predict the subjective quality of point cloud with superimposed geometry and color impairments. Motivated by the facts…

cs.CV2020

Dual Temporal Memory Network for Efficient Video Object Segmentation

Kaihua Zhang, Long Wang, Dong Liu +3

Video Object Segmentation (VOS) is typically formulated in a semi-supervised setting. Given the ground-truth segmentation mask on the first frame, the task of VOS is to track and s…

cs.CV2020464 cited

Classification of Hyperspectral and LiDAR Data Using Coupled CNNs

Renlong Hang, Zhu Li, Pedram Ghamisi +3

In this paper, we propose an efficient and effective framework to fuse hyperspectral and Light Detection And Ranging (LiDAR) data using two coupled convolutional neural networks (C…