most citedFast Hyperspectral Image Recovery via Non-iterative Fusion of Dual-Camera Compressive Hyperspectral Imaging

6 citations · 8 across the 5 of their papers we have counts for

collaborators

6 papers

cs.CV2021

A Miniature Biological Eagle-Eye Vision System for Small Target Detection

Shutai Wang, Qiang Fu, Yinhao Hu +2

Small target detection is known to be a challenging problem. Inspired by the structural characteristics and physiological mechanism of eagle-eye, a miniature vision system is desig…

cs.LG20212 cited

AlterSGD: Finding Flat Minima for Continual Learning by Alternative Training

Zhongzhan Huang, Mingfu Liang, Senwei Liang +1

Deep neural networks suffer from catastrophic forgetting when learning multiple knowledge sequentially, and a growing number of approaches have been proposed to mitigate this probl…

cs.CV2021

Blending Pruning Criteria for Convolutional Neural Networks

Wei He, Zhongzhan Huang, Mingfu Liang +2

The advancement of convolutional neural networks (CNNs) on various vision applications has attracted lots of attention. Yet the majority of CNNs are unable to satisfy the strict re…

cs.CV2021

Interpretable Hyperspectral AI: When Non-Convex Modeling meets Hyperspectral Remote Sensing

Danfeng Hong, Wei He, Naoto Yokoya +5

Hyperspectral imaging, also known as image spectrometry, is a landmark technique in geoscience and remote sensing (RS). In the past decade, enormous efforts have been made to proce…

eess.IV20206 cited

Fast Hyperspectral Image Recovery via Non-iterative Fusion of Dual-Camera Compressive Hyperspectral Imaging

Wei He, Naoto Yokoya, Xin Yuan

Coded aperture snapshot spectral imaging (CASSI) is a promising technique to capture the three-dimensional hyperspectral image (HSI) using a single coded two-dimensional (2D) measu…

cs.CV2020

Efficient Attention Network: Accelerate Attention by Searching Where to Plug

Zhongzhan Huang, Senwei Liang, Mingfu Liang +2

Recently, many plug-and-play self-attention modules are proposed to enhance the model generalization by exploiting the internal information of deep convolutional neural networks (C…