[1] TRAN D, BOURDEV L, FERGUS R, et al. Learning spatiotemporal features with 3D convolutional networks[C]//Proceedings of the 2015 IEEE International Conference on Computer Vision. Piscataway:IEEE, 2015:4489-4497. [2] ZHANG K, LIU N, YUAN X F, et al. Fine-grained age estimation in the wild with attention LSTM networks[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2020, 30(9):3140-3152. [3] YANG Z, LUO T G, WANG D, et al. Learning to navigate for finegrained classification[C]//Proceedings of the 2018 European Conference on Computer Vision, LNCS 11218. Cham:Springer, 2018:438-454. [4] BERG T, BELHUMEUR P N. POOF:part-based one-vs.-one features for fine-grained categorization, face verification, and attribute estimation[C]//Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE, 2013:955-962. [5] XIE L X, TIAN Q, HONG R C, et al. Hierarchical part matching for fine-grained visual categorization[C]//Proceedings of the 2013 IEEE International Conference on Computer Vision. Piscataway:IEEE, 2013:1641-1648. [6] ZHANG N, DONAHUE J, GIRSHICK R, et al. Part-based R-CNNs for fine-grained category detection[C]//Proceedings of the 2014 European Conference on Computer Vision, LNCS 8689. Cham:Springer, 2014:834-849. [7] GIRSHICK R, DONAHUE J, DARRELL T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]//Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE, 2014:580-587. [8] LIN D, SHEN X Y, LU C W, et al. Deep LAC:deep localization, alignment and classification for fine-grained recognition[C]//Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE, 2015:1666-1674. [9] LIN T Y, ROYCHOWDHURY A, MAJI S. Bilinear CNN models for fine-grained visual recognition[C]//Proceedings of the 2015 IEEE International Conference on Computer Vision. Piscataway:IEEE, 2015:1449-1457. [10] LIN T Y, MAJI S. Improved bilinear pooling with CNNs[C]//Proceedings of the 2017 British Machine Vision Conference. Durham:BMVA Press, 2017:No. 117. [11] LI P H, XIE J T, WANG Q L, et al. Towards faster training of global covariance pooling networks by iterative matrix square root normalization[C]//Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE, 2018:947-955. [12] JADERBERG M, SIMONYAN K, ZISSERMAN A, et al. Spatial transformer networks[C]//Proceedings of the 28th International Conference on Neural Information Processing Systems. Cambridge:MIT Press, 2015:2017-2025. [13] FU J L, ZHENG H L, MEI T. Look closer to see better:recurrent attention convolutional neural network for fine-grained image recognition[C]//Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE, 2017:4476-4484. [14] ZHENG H L, FU J L, MEI T, et al. Learning multi-attention convolutional neural network for fine-grained image recognition[C]//Proceedings of the 2017 IEEE International Conference on Computer Vision. Piscataway:IEEE, 2017:5219-5227. [15] ZEILER M D, FERGUS R. Visualizing and understanding convolutional networks[C]//Proceedings of the 2014 European Conference on Computer Vision, LNCS 8689. Cham:Springer, 2014:818-833. [16] ZHOU B L, KHOSLA A, LAPEDRIZA A, et al. Learning deep features for discriminative localization[C]//Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE, 2016:2921-2929. [17] LEE C Y, XIE S N, GALLAGHER P, et al. Deeply-supervised nets[C]//Proceedings of the 18th International Conference on Artificial Intelligence and Statistics. New York:JMLR. org, 2015:562-570. [18] JIANG Z L, WANG Y M, DAVIS L, et al. Learning discriminative features via label consistent neural network[C]//Proceedings of the 2017 IEEE Winter Conference on Applications of Computer Vision. Piscataway:IEEE, 2017:207-216. [19] JIN X J, CHEN Y P, DONG J, et al. Collaborative layer-wise discriminative learning in deep neural networks[C]//Proceedings of the 2016 European Conference on Computer Vision, LNCS 9911. Cham:Springer, 2016:733-749. [20] LIU W, ANGUELOV D, ERHAN D, et al. SSD:single shot multiBox detector[C]//Proceedings of the 2016 European Conference on Computer Vision, LNCS 9905. Cham:Springer, 2016:21-37. [21] ZHANG X P, XIONG H K, ZHOU W G, et al. Picking deep filter responses for fine-grained image recognition[C]//Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE, 2016:1134-1142. [22] WANG Y M, MORARIU V I, DAVIS L S. Learning a discriminative filter bank within a CNN for fine-grained recognition[C]//Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE, 2018:4148-4157. [23] GATYS L A, ECKER A S, BETHGE M. Image style transfer using convolutional neural networks[C]//Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE, 2016:2414-2423. [24] HU T, QI H G, HUANG Q M, et al. See better before looking closer:weakly supervised data augmentation network for finegrained visual classification[EB/OL]. (2019-03-23)[2020-03-23]. https://arxiv.org/pdf/1901.09891.pdf. [25] SIMONYAN K, ZISSERMAN A. Very deep convolutional networks for large-scale image recognition[EB/OL]. (2015-04-10)[2020-03-20]. https://arxiv.org/pdf/1409.1556.pdf. [26] HE K M, ZHANG X Y, REN S Q, et al. Deep residual learning for image recognition[C]//Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE, 2016:770-778. [27] 张雪芹, 余丽君. 基于判别关键域和深度学习的植物图像分类[J]. 计算机工程与设计, 2020, 41(3):742-748.(ZHANG X Q, YU L J. Classification of plant images based on discriminating key domains and deep learning[J]. Computer Engineering and Design, 2020, 41(3):742-748.) [28] 姜代红, 张三友, 刘其开. 基于特征重标定生成对抗网络的图像分类算法[J]. 计算机应用研究, 2020, 37(3):932-935. (JIANG D H, ZHANG S Y, LIU Q K. Image classification algorithm based on feature recalibration GAN[J]. Application Research of Computers, 2020, 37(3):932-935.) [29] 孙敏, 李旸, 庄正飞, 等. 基于并行混合网络融入注意力机制的情感分析[J]. 计算机应用, 2020, 40(9):2543-2548.(SUN M, LI Y, ZHUANG Z F, et al. Sentiment analysis based on parallel hybrid network and attention mechanism[J]. Journal of Computer Applications, 2020, 40(9):2543-2548.) [30] 边小勇, 江沛龄, 赵敏, 等. 基于多分支神经网络模型的弱监督细粒度图像分类方法[J]. 计算机应用, 2020, 40(5):1295-1300.(BIAN X Y, JIANG P L, ZHAO M, et al. Multi-branch neural network model based weakly supervised fine-grained image classification method[J]. Journal of Computer Applications, 2020, 40(5):1295-1300.) |