Pairnorm based Graphical Convolution Network for zero-shot multi-label classification
Tài liệu tham khảo
Akata, Z., Perronnin, F., Harchaoui, Z., Schmid, C., 2013. Label-embedding for attribute-based classification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 819–826.
Akata, 2015, Label-embedding for image classification, IEEE Trans. Pattern Anal. Mach. Intell., 38, 1425, 10.1109/TPAMI.2015.2487986
Arifoglu, 2020, Detecting indicators of cognitive impairment via Graph Convolutional Networks, Eng. Appl. Artif. Intell., 89, 10.1016/j.engappai.2019.103401
Cerri, 2014, Hierarchical multi-label classification using local neural networks, J. Comput. System Sci., 80, 39, 10.1016/j.jcss.2013.03.007
Chauhan, 2022, Randomized neural networks for multilabel classification, Appl. Soft Comput., 115, 10.1016/j.asoc.2021.108184
Chauhan, 2020, Multi-label classifier based on kernel random vector functional link network, 1
Chen, 2008, Semi-supervised multi-label learning by solving a sylvester equation, 410
Chen, Z.-M., Wei, X.-S., Wang, P., Guo, Y., 2019. Multi-label image recognition with graph convolutional networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5177–5186.
Chua, T.-S., Tang, J., Hong, R., Li, H., Luo, Z., Zheng, Y., 2009. Nus-wide: a real-world web image database from national university of singapore. In: Proceedings of the ACM International Conference on Image and Video Retrieval, pp. 1–9.
Deng, 2014, Large-scale object classification using label relation graphs, 48
Ding, 2022, Temporal segment graph convolutional networks for skeleton-based action recognition, Eng. Appl. Artif. Intell., 110, 10.1016/j.engappai.2022.104675
Elisseeff, 2001, A kernel method for multi-labelled classification, 681
Frome, 2013, Devise: A deep visual-semantic embedding model, 2, 2121
Fu, 2015, Transductive multi-view zero-shot learning, IEEE Trans. Pattern Anal. Mach. Intell., 37, 2332, 10.1109/TPAMI.2015.2408354
Gaure, 2017, A probabilistic framework for zero-shot multi-label learning, 3
Kipf, 2016
Lampert, 2013, Attribute-based classification for zero-shot visual object categorization, IEEE Trans. Pattern Anal. Mach. Intell., 36, 453, 10.1109/TPAMI.2013.140
Law, 2021, Multi-label classification using binary tree of classifiers, IEEE Trans. Emerg. Top. Comput. Intell., 1
Lee, C.-W., Fang, W., Yeh, C.-K., Wang, Y.-C.F., 2018. Multi-label zero-shot learning with structured knowledge graphs. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1576–1585.
Li, Q., Han, Z., Wu, X.-M., 2018. Deeper insights into graph convolutional networks for semi-supervised learning. In: Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, pp. 3538–3545.
Lin, 2014, Microsoft coco: Common objects in context, 740
Liu, 2021, MFDNet: COllaborative poses perception and matrix Fisher distribution for head pose estimation, IEEE Trans. Multimed.
Liu, 2019, RISIR: RApid infrared spectral imaging restoration model for industrial material detection in intelligent video systems, IEEE Trans. Ind. Inf., 1, 10.1109/TII.2019.2930463
Liu, 2019, Cross-modal zero-shot hashing, 449
Liu, 2019, Flexible FTIR spectral imaging enhancement for industrial robot infrared vision sensing, IEEE Trans. Ind. Inf., 16, 544, 10.1109/TII.2019.2934728
Liu, 2022, ARHPE: ASymmetric relation-aware representation learning for head pose estimation in industrial human-machine interaction, IEEE Trans. Ind. Inf.
Liu, 2021, Facial expression recognition method with multi-label distribution learning for non-verbal behavior understanding in the classroom, Infrared Phys. Technol., 112, 10.1016/j.infrared.2020.103594
Liu, 2022, EDMF: EFficient deep matrix factorization with review feature learning for industrial recommender system, IEEE Trans. Ind. Inf., 18, 4361, 10.1109/TII.2021.3128240
Liu, 2022, Multi-perspective social recommendation method with graph representation learning, Neurocomputing, 468, 469, 10.1016/j.neucom.2021.10.050
Luo, 2017, A multi-label classification algorithm based on kernel extreme learning machine, Neurocomputing, 260, 313, 10.1016/j.neucom.2017.04.052
Makadia, 2010, Baselines for image annotation, Int. J. Comput. Vis., 90, 88, 10.1007/s11263-010-0338-6
Mensink, T., Gavves, E., Snoek, C.G., 2014. Costa: Co-occurrence statistics for zero-shot classification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2441–2448.
Mikolov, 2013, Efficient estimation of word representations in vector space, 1
Ou, 2020, Multi-label zero-shot learning with graph convolutional networks, Neural Netw., 132, 333, 10.1016/j.neunet.2020.09.010
Pan, 2009, A survey on transfer learning, IEEE Trans. Knowl. Data Eng., 22, 1345, 10.1109/TKDE.2009.191
Pennington, J., Socher, R., Manning, C.D., 2014. Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543.
Puzanov, 2020, Deep reinforcement one-shot learning for artificially intelligent classification in expert aided systems, Eng. Appl. Artif. Intell., 91, 10.1016/j.engappai.2020.103589
Romera-Paredes, 2015, An embarrassingly simple approach to zero-shot learning, 2152
Schuster, 1997, Bidirectional recurrent neural networks, IEEE Trans. Signal Process., 45, 2673, 10.1109/78.650093
Shao, 2019, Zero-shot multi-label learning via label factorisation, IET Comput. Vis., 13, 117, 10.1049/iet-cvi.2018.5131
Song, J., Shen, C., Yang, Y., Liu, Y., Song, M., 2018. Transductive unbiased embedding for zero-shot learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1024–1033.
Tai, 2012, Multilabel classification with principal label space transformation, Neural Comput., 24, 2508, 10.1162/NECO_a_00320
Tan, 2017, Semi-supervised multi-label classification using incomplete label information, Neurocomputing, 260, 192, 10.1016/j.neucom.2017.04.033
Wang, 2021, Covid-19 classification by FGCNet with deep feature fusion from graph convolutional network and convolutional neural network, Inf. Fusion, 67, 208, 10.1016/j.inffus.2020.10.004
Weston, J., Bengio, S., Usunier, N., 2011. WSABIE: Scaling up to large vocabulary image annotation. In: Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, pp. 2764–2770.
Wright, 1995, Logistic regression, Read. Underst. Multivariate Statist., 217
Xian, 2018, Zero-shot learning—a comprehensive evaluation of the good, the bad and the ugly, IEEE Trans. Pattern Anal. Mach. Intell., 41, 2251, 10.1109/TPAMI.2018.2857768
Xie, G.-S., Liu, L., Jin, X., Zhu, F., Zhang, Z., Qin, J., Yao, Y., Shao, L., 2019. Attentive region embedding network for zero-shot learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9384–9393.
Xu, 2022, Instance segmentation of biological images using graph convolutional network, Eng. Appl. Artif. Intell., 110, 10.1016/j.engappai.2022.104739
Yeh, 2020, Multilabel deep visual-semantic embedding, IEEE Trans. Pattern Anal. Mach. Intell., 42, 1530, 10.1109/TPAMI.2019.2911065
Yu, 2018, Feature-induced partial multi-label learning, 1398
Zhang, 2016, Fast zero-shot image tagging, 5985
Zhang, 2021, Improved breast cancer classification through combining graph convolutional network and convolutional neural network, Inf. Process. Manage., 58, 10.1016/j.ipm.2020.102439
Zhang, 2006, Multilabel neural networks with applications to functional genomics and text categorization, IEEE Trans. Knowl. Data Eng., 18, 1338, 10.1109/TKDE.2006.162
Zhang, 2007, ML-KNN: A Lazy learning approach to multi-label learning, Pattern Recognit., 40, 2038, 10.1016/j.patcog.2006.12.019
Zhang, 2013, A review on multi-label learning algorithms, IEEE Trans. Knowl. Data Eng., 26, 1819, 10.1109/TKDE.2013.39
Zhao, 2019
Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., Torralba, A., 2016. Learning deep features for discriminative localization. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2921–2929.