[4] Zhang Q W, Zhong Y, Zhang M L. Feature-induced labeling information enrichment for multi-label learning[C]∥ 32th AAAI Conference on Artificial Intelligence, February 2-7, 2018, New Orleans, Louisiana, USA. Reston,, 4446-4453(2018).
[5] Nie L Q, Wang X, Zhang J L et al. Enhancing micro-video understanding by harnessing external sounds[C]∥Proceedings of the 2017 ACM on Multimedia Conference-MM ’17, October 19-27, 2017. Mountain View, California, USA., 1192-1200(2017).
[12] Hassannejad H, Matrella G, Ciampolini P et al. Food image recognition using very deep convolutional networks[C]∥Proceedings of the 2nd International Workshop on Multimedia Assisted Dietary Management-MADiMa ’16, October 16, 2016, Amsterdam, The Ne, 41-49(2016).
[13] Wang L M, Qiao Y, Tang X O. Action recognition with trajectory-pooled deep-convolutional descriptors[C]∥2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 7-12, 2015, Boston, MA, USA., 4305-4314(2015).
[14] Jia Z L, Zhang X, Guan N Y et al. Gene ranking of RNA-seq data via discriminant non-negative matrix factorization[J]. PLoS One, 10, e0137782(2015).
[15] Liu G C, Yan S C. Latent Low-Rank Representation for subspace segmentation and feature extraction[C]∥2011 International Conference on Computer Vision, November 6-13, 2011, Barcelona, Spain., 1615-1622(2011).
[16] Zhu Y, Kwok J T, Zhou Z H. Multi-label learning with global and local label correlation[J]. IEEE Transactions on Knowledge and Data Engineering, 30, 1081-1094(2018).
[18] Szegedy C, Liu W, Jia Y et al. Going deeper with convolutions[C]∥2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 7-12, 2015, Boston, MA, USA., 1-9(2015).
[19] Tran D, Bourdev L, Fergus R et al. Learning spatiotemporal features with 3D convolutional networks[C]∥2015 IEEE International Conference on Computer Vision (ICCV), December 7-13, 2015, Santiago, Chile., 4489-4497(2015).
[20] Yeh C K, Wu W C, Ko W J et al[C]. Learning deep latent spaces for multi-label classification 31th AAAI Conference on Artificial Intelligence, February 4-9, 2017, San Francisco, California. Reston,, 2838-2844(2017).