Pairnorm based Graphical Convolution Network for zero-shot multi-label classification

Engineering Applications of Artificial Intelligence - Tập 114 - Trang 105012 - 2022
Vikas Chauhan1, Aruna Tiwari1
1Department of Computer Science and Engineering, Indian Institute of Technology Indore, India

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.