most citedMulti-scale Dynamic Graph Convolutional Network for Hyperspectral Image Classification

12 citations · 21 across the 5 of their papers we have counts for

collaborators

5 papers

eess.IV201912 cited

Multi-scale Dynamic Graph Convolutional Network for Hyperspectral Image Classification

Sheng Wan, Chen Gong, Ping Zhong +3

Convolutional Neural Network (CNN) has demonstrated impressive ability to represent hyperspectral images and to achieve promising results in hyperspectral image classification. How…

cs.LG2019

Robust and Discriminative Labeling for Multi-label Active Learning Based on Maximum Correntropy Criterion

Bo Du, Zengmao Wang, Lefei Zhang +2

Multi-label learning draws great interests in many real world applications. It is a highly costly task to assign many labels by the oracle for one instance. Meanwhile, it is also h…

cs.LG2019

Exploring Representativeness and Informativeness for Active Learning

Bo Du, Zengmao Wang, Lefei Zhang +4

How can we find a general way to choose the most suitable samples for training a classifier? Even with very limited prior information? Active learning, which can be regarded as an…

cs.CV20194 cited

Simultaneous Spectral-Spatial Feature Selection and Extraction for Hyperspectral Images

Lefei Zhang, Qian Zhang, Bo Du +3

In hyperspectral remote sensing data mining, it is important to take into account of both spectral and spatial information, such as the spectral signature, texture feature and morp…

cs.CV20195 cited

Fast Spatio-Temporal Residual Network for Video Super-Resolution

Sheng Li, Fengxiang He, Bo Du +3

Recently, deep learning based video super-resolution (SR) methods have achieved promising performance. To simultaneously exploit the spatial and temporal information of videos, emp…