[1] KRIZHEVSKY A, SUTSKEVER I, HINTON G E.ImageNet classification with deep convolutional neural networks[J].Communications of the ACM, 2017, 60(6):84-90. [2] SIMONYAN K, ZISSERMAN A.Very deep convolutional networks for large-scale image recognition[EB/OL].[2021-06-11].https://arxiv.org/abs/1409.1556. [3] HE K M, ZHANG X Y, REN S Q, et al.Deep residual learning for image recognition[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C., USA:IEEE Press, 2016:770-778. [4] ZAGORUYKO S, KOMODAKIS N.Wide residual networks[C]//Proceedings of British Machine Vision Conference.[S.l.]:BMVC, 2016:1-12. [5] GAO S H, CHENG M M, ZHAO K, et al.Res2Net:a new multi-scale backbone architecture[J].IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021, 43(2):652-662. [6] YUAN P C, LIN S F, CUI C, et al.HS-ResNet:hierarchical-split block on convolutional neural network[EB/OL].[2021-06-11].https://arxiv.org/abs/2010.07621. [7] HE T, ZHANG Z, ZHANG H, et al.Bag of tricks for image classification with convolutional neural networks[C]//Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition.Washington D.C., USA:IEEE Press, 2019:558-567. [8] HAN D, KIM J, KIM J.Deep pyramidal residual networks[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C., USA:IEEE Press, 2017:6307-6315. [9] 任长娥, 袁超, 孙彦丽, 等.宽度学习系统研究进展[J].计算机应用研究, 2021, 38(8):2258-2267. REN C G, YUAN C, SUN Y L, et al.Research of broad learning system[J].Application Research of Computers, 2021, 38(8):2258-2267.(in Chinese) [10] 黎玲利, 孟令兵, 李金宝.多尺度特征提取和多级别特征融合的显著性目标检测方法[J].工程科学与技术, 2021, 53(1):170-177. LI L L, MENG L B, LI J B.Salient object detection based on multi-scale feature extraction and multi-level feature fusion[J].Advanced Engineering Sciences, 2021, 53(1):170-177.(in Chinese) [11] 官申珂, 林晓, 郑晓妹, 等.结合超像素分割的多尺度特征融合图像语义分割算法[J].图学学报, 2021, 42(3):406-413. GUAN S K, LIN X, ZHENG X M, et al.A semantic segmentation algorithm using multi-scale feature fusion with combination of superpixel segmentation[J].Journal of Graphics, 2021, 42(3):406-413.(in Chinese) [12] 任欢, 王旭光.注意力机制综述[J].计算机应用, 2021, 41(S1):1-6. REN H, WANG X G.Review of attention mechanism[J].Journal of Computer Applications, 2021, 41(S1):1-6.(in Chinese) [13] 赵升, 赵黎.基于双向特征金字塔和深度学习的图像识别方法[J].哈尔滨理工大学学报, 2021, 26(2):44-50. ZHAO S, ZHAO L.On image recognition using bidirectional feature pyramid and deep neural network[J].Journal of Harbin University of Science and Technology, 2021(2):44-50.(in Chinese) [14] 朱旭东, 熊贇.基于多层次注意力和图模型的图像多标签分类研究[J/OL].计算机工程:1-8[2021-06-11].DOI:10.19678/j.issn.1000-3428.0061072. ZHU X D, XIONG Y.Multi-label image classification method based on multi scale attention and graph model[J/OL].Computer Engineering:1-8[2021-06-11].DOI:10.19678/j.issn.1000-3428.0061072.(in Chinese) [15] 吴旭, 刘翔, 赵静文.一种轻量级多尺度融合的图像篡改检测算法[J].计算机工程, 2022, 48(2):224-229, 236. WU X, LIU X, ZHAO J W.A lightweight multiscale fusion algorithm for image tampering detection[J].Computer Engineering, 2022, 48(2):224-229, 236.(in Chinese) [16] 王柳程, 欧阳城添, 梁文.基于改进特征金字塔网络的人体姿态估计[J].计算机工程, 2021, 47(8):251-259, 270. WANG L C, OUYANG C T, LIANG W.Human pose estimation based on improved pyramid feature network[J].Computer Engineering, 2021, 47(8):251-259, 270.(in Chinese) [17] LIN T Y, DOLLÁR P, GIRSHICK R, et al.Feature pyramid networks for object detection[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C., USA, USA:IEEE Press, 2017:936-944. [18] HAN K, WANG Y H, TIAN Q, et al.GhostNet:more features from cheap operations[C]//Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition.Washington D.C., USA, USA:IEEE Press, 2020:1577-1586. [19] KRIZHEVSKY A, HINTON G.Learning multiple layers of features from tiny images[J].Handbook of Systemic Autoimmune Diseases, 2009, 1(4):1-5. [20] LUO W G, LI Y J, URTASUN R, et al.Understanding the effective receptive field in deep convolutional neural networks[EB/OL].[2021-06-11].https://arxiv.org/pdf/1701.04128v1.pdf. [21] CAO X.A practical theory for designing very deep convo-lutional neural networks[EB/OL].[2021-06-11].http://pdfs.semanticscholar.org/7922/2fad9f671be142bd7e42cd785a2cb06a1d30.pdf. [22] HE K M, ZHANG X Y, REN S Q, et al.Delving deep into rectifiers:surpassing human-level performance on ImageNet classification[C]//Proceedings of IEEE International Conference on Computer Vision.Washington D.C., USA, USA:IEEE Press, 2015:1026-1034. |