[1] 张坤华, 谭志恒, 李斌. 结合粒子群优化和综合评价的脉冲耦合神经网络图像自动分割 ［J］. 光学 精密工程, 2018, 26(4): 962-970.

    ZHANG K H, TAN ZH H, LI B. Automated image segmentation based on pulse coupled neural network with partide swarm optimization and comprehensive evaluation ［J］. Opt. Precision Eng., 2018, 26(4): 962-970. (in Chinese)

[2] 刘智, 黄江涛, 冯欣.构建多尺度深度卷积神经网络行为识别模型 ［J］.光学 精密工程, 2017, 25(3): 799-805.

    LIU ZH, HUANG J T, FENG X. Action recognition model construction based on multi-scale deep convolution neural network ［J］. Opt. Precision Eng., 2017, 25(3): 799-805. (in Chinese)

[3] 李宇, 刘雪莹, 张洪群, 等.基于卷积神经网络的光学遥感图像检索 ［J］. 光学 精密工程, 2018, 26(1): 200-207.

    LI Y, LIU X Y, ZHANG H Q, et al.. Optical remote sensing image retrieval based on convolutional neural networks ［J］. Opt. Precision Eng. , 2018, 26(1): 200-207. (in Chinese)

[4] WEI Y, XIA W, LIN M, et al.. HCP: A flexible CNN framework for multi-label image classification ［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 38(9): 1901-1907.

[5] WANG Z, CHEN T, LI G, et al.. Multi-label image recognition by recurrently discovering attentional regions ［C］. Proceedings of the IEEE International Conference on Computer Vision (CVPR), 2017: 464-472.

[6] YU W J, CHEN ZH D, LUO X, et al.. DELTA: A deep dual-stream network for multi-label image classification ［J］.Pattern Recognition, 2019, 91: 322-331.

[7] YAN Z, LIU W W, WEN SH P, et al.. Multi-label image classification by feature attention network ［J］. IEEE Access, 2019, 7: 98005-98013.

[8] SONG P, JING L P, et al.. Exploiting label relationships in multi-label classification with neural networks ［J］. Journal of Computer Research and Development. 2018, 55(8): 1751-1759.

[9] JIN J, NAKAYAMA H. Annotation order matters: Recurrent image annotator for arbitrary length image tagging ［C］. 2016 23rd International Conference on Pattern Recognition (ICPR), IEEE, 2016: 2452-2457.

[10] WANG J, YANG Y, MAO J, et al.. Cnn-rnn: A unified framework for multi-label image classification ［C］. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016: 2285-2294.

[11] ZHAO B, LI X, LU X, et al.. A CNN-RNN architecture for multi-label weather recognition ［J］. Neurocomputing, 2018, 322: 47-57.

[12] LYU F, HU F, SHENG V S, et al.. Coarse to fine: multi-label image classification with global/local attention ［C］. 2018 IEEE International Smart Cities Conference (ISC2), IEEE, 2018: 1-7.

[13] ZEILER M D, FERGUS R. Visualizing and understanding convolutional networks ［C］. European Conference on Computer Vision. Springer, Cham, 2014: 818-833.

[14] SZEGEDY C, VANHOUCKE V, IOFFE S, et al.. Rethinking the inception architecture for computer vision ［C］. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016: 2818-2826.

[15] ZHOU ZH H, ZHANG M L. Multi-instance multi-label learning with application to scene classification ［C］. Advances in Neural Information Processing Systems, 2007: 1609-1616.

[16] DONG M, PANG K, WU Y, et al.. Transferring CNNS to multi-instance multi-label classification on small datasets ［C］. 2017 IEEE International Conference on Image Processing (ICIP), IEEE, 2017: 1332-1336.

[17] LI Y F, HU J H, JIANG Y, et al.. Towards discovering what patterns trigger what labels ［C］. Twenty-Sixth AAAI Conference on Artificial Intelligence (AAAI), 2012: 1012-1018.

[18] HUANG S J, GAN W, ZHOU ZH H. Fast multi-instance multi-label learning ［C］. Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence (AAAI), 2014: 1868-1874.

[19] WANG T Z, HUANG S J, ZHOU ZH H. Towards identifying causal relation between instances and labels ［C］. Proceedings of the 2019 SIAM International Conference on Data Mining. Society for Industrial and Applied Mathematics, 2019: 289-297.

[20] LIU K, WANG H, NIE F P, et al.. Learning multi-instance enriched image representations via non-greedy ratio maximization of the L1-norm distances ［C］. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018: 7727-7735.