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Newton design:designing CNNs with the family of Newton's methods被引量：1
《Science China(Information Sciences)》2023年第6期118-133,共16页Zhengyang SHEN Yibo YANG Qi SHE Changhu WANG Jinwen MA Zhouchen LIN 
supported by National Key R&D Program of China (Grant No.2022ZD0160302);Major Key Project of PCL,China (Grant No.PCL2021A12);National Natural Science Foundation of China (Grant No.62276004)。
Nowadays,convolutional neural networks(CNNs)have led the developments of machine learning.However,most CNN architectures are obtained by manual design,which is empirical,time-consuming,and non-transparent.In this pape...
关键词：CNN DROPOUT optimization method network design Newton's method 
RGA-CNNs:convolutional neural networks based on reduced geometric algebra被引量：1
《Science China(Information Sciences)》2021年第2期236-238,共3页Rui WANG Miaomiao SHEN Xiangyang WANG Wenming CAO 
supported by National Natural Science Foundation of China(Grant Nos.61771299,61771322,61375015,61301027)。
Dear editor,Recently,convolutional neural networks(CNNs)have exhibited high performance particularly in object detection[1],face recognition[2],and image classification[3].However,there has been little work on CNN mod...
关键词：NETWORKS NEURAL CNNS 
A new sensor bias-driven spatio-temporal fusion model based on convolutional neural networks被引量：10
《Science China(Information Sciences)》2020年第4期20-35,共16页Yunfei LI Jun LI Lin HE Jin CHEN Antonio PLAZA 
supported in part by National Natural Science Foundation of China(Grant Nos.61771496,61571195,61901208);National Key Research and Development Program of China(Grant No.2017YFB0502900);Guangdong Provincial Natural Science Foundation(Grant Nos.2016A030313254,2017A030313382);Science and Technology Project of Jiangxi Provincial Department of Education(Grant No.GJJ180962);Natural Science Foundation of Jiangxi China(Grant No.20192BAB217003)。
Owing to the tradeoff between scanning swath and pixel size,currently no satellite Earth observation sensors are able to collect images with high spatial and temporal resolution simultaneously.This limits the applicat...
关键词：SPATIO-TEMPORAL fusion(STF) convolutional NEURAL networks(CNNs) sensor bias-driven STF 
Hybrid first and second order attention Unet for building segmentation in remote sensing images被引量：21
《Science China(Information Sciences)》2020年第4期65-76,共12页Nanjun HE Leyuan FANG Antonio PLAZA 
supported in part by National Natural Science Foundation of China(Grant Nos.61922029,61771192);National Natural Science Foundation of China for International Cooperation and Exchanges(Grant No.61520106001);Huxiang Young Talents Plan Project of Hunan Province(Grant No.2019RS2016)。
Recently,building segmentation(BS)has drawn significant attention in remote sensing applications.Convolutional neural networks(CNNs)have become the mainstream analysis approach in this field owing to their powerful re...
关键词：BUILDING segmentation(BS) convolutional neural networks(CNNs) remote sensing high order pooling ATTENTION 
Deterministic conversion rule for CNNs to efficient spiking convolutional neural networks被引量：2
《Science China(Information Sciences)》2020年第2期196-214,共19页Xu YANG Zhongxing ZHANG Wenping ZHU Shuangming YU Liyuan LIU Nanjian WU 
supported by National Natural Science Foundation of China(Grant Nos.61704167,61434004);Beijing Municipal Science and Technology Project(Grant No.Z181100008918009);Youth Innovation Promotion Association Program,Chinese Academy of Sciences(Grant No.2016107);Strategic Priority Research Program of Chinese Academy of Science(Grant No.XDB32050200).
This paper proposes a general conversion theory to reveal the relations between convolutional neural network(CNN)and spiking convolutional neural network(spiking CNN)from structure to information processing.Based on t...
关键词：convolutional NEURAL networks(CNN) SPIKING NEURAL networks(SNN) image classification CONVERSION RULE noise robustness neuromorphic hardware 
Survey of recent progress in semantic image segmentation with CNNs被引量：12
《Science China(Information Sciences)》2018年第5期103-120,共18页Qichuan GENG Zhong ZHOU Xiaochun CAO 
supported by National High-tech R&D Program of China (863 Program) (Grant No. 2015AA016403);National Natural Science Foundation of China (Grant Nos. 61572061, 61472020)
In recent years, convolutional neural networks （CNNs） are leading the way in many computer vision tasks, such as image classification, object detection, and face recognition. In order to produce more refined semanti...
关键词：semantic image segmentation CNN Pascal VOC 2012 challenge multi-granularity features construction of contextual relationships 
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