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20182024
most citedHyperspectral Classification Based on Lightweight 3-D-CNN With Transfer Learning

228 citations · 235 across the 4 of their papers we have counts for

collaborators

7 papers

cs.CV2020228 cited

Hyperspectral Classification Based on Lightweight 3-D-CNN With Transfer Learning

Haokui Zhang, Ying Li, Yenan Jiang +3

Recently, hyperspectral image (HSI) classification approaches based on deep learning (DL) models have been proposed and shown promising performance. However, because of very limite…

cs.CV2020

Memory-Efficient Hierarchical Neural Architecture Search for Image Restoration

Haokui Zhang, Ying Li, Hao Chen +3

Recently, much attention has been spent on neural architecture search (NAS), aiming to outperform those manually-designed neural architectures on high-level vision recognition task…

cs.CV20202 cited

Hyperspectral Image Classification with Spatial Consistence Using Fully Convolutional Spatial Propagation Network

Yenan Jiang, Ying Li, Shanrong Zou +2

In recent years, deep convolutional neural networks (CNNs) have shown impressive ability to represent hyperspectral images (HSIs) and achieved encouraging results in HSI classifica…

cs.CV20205 cited

Hyperspectral Classification Based on 3D Asymmetric Inception Network with Data Fusion Transfer Learning

Haokui Zhang, Yu Liu, Bei Fang +3

Hyperspectral image(HSI) classification has been improved with convolutional neural network(CNN) in very recent years. Being different from the RGB datasets, different HSI datasets…

cs.CV2019

Memory-Efficient Hierarchical Neural Architecture Search for Image Denoising

Haokui Zhang, Ying Li, Hao Chen +1

Recently, neural architecture search (NAS) methods have attracted much attention and outperformed manually designed architectures on a few high-level vision tasks. In this paper, w…

cs.CV2019

Exploiting temporal consistency for real-time video depth estimation

Haokui Zhang, Chunhua Shen, Ying Li +3

Accuracy of depth estimation from static images has been significantly improved recently, by exploiting hierarchical features from deep convolutional neural networks (CNNs). Compar…