Deep low-rank matrix factorization with latent correlation estimation for micro-video multi-label classification
Tóm tắt
Từ khóa
Tài liệu tham khảo
Zhang, 2016, Shorter-is-better: Venue category estimation from micro-video, 1415
Liu, 2019, Joint learning of nnextvlad, cnn and context gating for micro-video venue classification, IEEE Access, 7, 77091, 10.1109/ACCESS.2019.2922430
Jing, 2017, Low-rank multi-view embedding learning for micro-video popularity prediction, IEEE Transactions on Knowledge and Data Engineering, 30, 1519, 10.1109/TKDE.2017.2785784
Chen, 2016, Micro tells macro: Predicting the popularity of micro-videos via a transductive model, 898
Wei, 2019, Personalized hashtag recommendation for micro-videos, 1446
Chen, 2019, Implicit rating methods based on interest preferences of categories for micro-video recommendation, 371
Nie, 2017, Enhancing micro-video understanding by harnessing external sounds, 1192
Xie, 2020, A multimodal variational encoder-decoder framework for micro-video popularity prediction, 2542
Liu, 2018, Online data organizer: micro-video categorization by structure-guided multimodal dictionary learning, IEEE Transactions on Image Processing, 28, 1235, 10.1109/TIP.2018.2875363
Wei, 2020, Neural multimodal cooperative learning toward micro-video understanding, IEEE Transactions on Image Processing, 29, 1, 10.1109/TIP.2019.2923608
Chen, 2018, Temporal hierarchical attention at category-and item-level for micro-video click-through prediction, 1146
Liu, 2019, User-video co-attention network for personalized micro-video recommendation, Proceedings of International World Wide Web Conference, 3020, 10.1145/3308558.3313513
Li, 2016, Weakly supervised deep matrix factorization for social image understanding, IEEE Transactions on Image Processing, 26, 276, 10.1109/TIP.2016.2624140
Xue, 2017, Deep matrix factorization models for recommender systems, 3203
Zhao, 2017, Multi-view clustering via deep matrix factorization, in, 2921
Lee, 1999, Learning the parts of objects by non-negative matrix factorization, Nature, 401, 788, 10.1038/44565
Ding, 2008, Convex and semi-nonnegative matrix factorizations, IEEE Transactions on Pattern Analysis and Machine Intelligence, 32, 45, 10.1109/TPAMI.2008.277
Trigeorgis, 2016, A deep matrix factorization method for learning attribute representations, IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 417, 10.1109/TPAMI.2016.2554555
Ahmed, 2018, A context integrated model for multi-label emotion detection, Procedia Computer Science, 142, 61
N.O.-S.X. Oramas S, Barbieri F, Multimodal deep learning for music genre classification, Transactions of the International Society for Music Information Retrieval 1 (1) (2018) 4–21.
Tang, 2019, Adaptive hypergraph embedded semi-supervised multi-label image annotation, IEEE Transactions on Multimedia, 21, 2837, 10.1109/TMM.2019.2909860
Boutell, 2004, Learning multi-label scene classification, Pattern Recognition, 37, 1757, 10.1016/j.patcog.2004.03.009
Fürnkranz, 2008, Multilabel classification via calibrated label ranking, Machine Learning, 73, 133, 10.1007/s10994-008-5064-8
Qi, 2007, Correlative multi-label video annotation, 17
Read, 2011, Classifier chains for multi-label classification, Machine Learning, 85, 333, 10.1007/s10994-011-5256-5
Shi, 2020, Mlne: Multi-label network embedding, IEEE Transactions on Neural Networks and Learning Systems, 31, 3682, 10.1109/TNNLS.2019.2945869
Liu, 2012, Robust recovery of subspace structures by low-rank representation, IEEE Transactions on Pattern Analysis and Machine Intelligence, 35, 171, 10.1109/TPAMI.2012.88
Li, 2018, Self-taught low-rank coding for visual learning, IEEE Transactions on Neural Networks and Learning Systems, 29, 645, 10.1109/TNNLS.2016.2633275
Xu, 2015, Discriminative transfer subspace learning via low-rank and sparse representation, IEEE Transactions on Image Processing, 25, 850, 10.1109/TIP.2015.2510498
Liu, 2010, Robust subspace segmentation by low-rank representation., in, 663
Torralba, 2003, Contextual priming for object detection, International Journal of Computer Vision, 53, 169, 10.1023/A:1023052124951
Zheng, 2014, Dense semantic image segmentation with objects and attributes, 3214
S. Bengio, J. Dean, D. Erhan, E. Ie, Q. Le, A. Rabinovich, J. Shlens, Y. Singer, Using web co-occurrence statistics for improving image categorization, arXiv preprint arXiv:1312.5697 (2013).
Modiri Assari, 2014, Video classification using semantic concept co-occurrences, in, 2529
Jiang, 2017, Exploiting feature and class relationships in video categorization with regularized deep neural networks, IEEE Transactions on Pattern Analysis and Machine Intelligence, 40, 352, 10.1109/TPAMI.2017.2670560
Jiang, 2012, Fast semantic diffusion for large-scale context-based image and video annotation, IEEE Transactions on Image Processing, 21, 3080, 10.1109/TIP.2012.2188038
Mensink, 2014, Costa: Co-occurrence statistics for zero-shot classification, 2441
Zhang, 2010, A convex formulation for learning task relationships in multi-task learning, in, 733
Cai, 2010, A singular value thresholding algorithm for matrix completion, SIAM Journal on Optimization, 20, 1956, 10.1137/080738970
Szegedy, 2016, Rethinking the inception architecture for computer vision, in, 2818
Zhang, 2007, Ml-knn: A lazy learning approach to multi-label learning, Pattern Recognition, 40, 2038, 10.1016/j.patcog.2006.12.019
Tran, 2015, Learning spatiotemporal features with 3d convolutional networks, 4489
Yeh, 2017, Learning deep latent space for multi-label classification, 2838
Li, 2015, Learning robust and discriminative subspace with low-rank constraints, IEEE Transactions on Neural Networks and Learning Systems, 27, 2160, 10.1109/TNNLS.2015.2464090
Liu, 2011, Latent low-rank representation for subspace segmentation and feature extraction, 1615
Chang, 2011, Libsvm: A library for support vector machines, ACM Transactions on Intelligent Systems and Technology, 2, 1, 10.1145/1961189.1961199
Min-Ling Zhang, 2009, Feature selection for multi-label naive bayes classification, Information Sciences, 179, 3218, 10.1016/j.ins.2009.06.010
Zhu, 2018, Multi-label learning with global and local label correlation, IEEE Transactions on Knowledge and Data Engineering, 30, 1081, 10.1109/TKDE.2017.2785795
Sun, 2016, A scalable clustering-based local multi-label classification method, 261