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    Multi-label zero-shot learning with graph convolutional networks

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    MZSL_GCN.pdf
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    Description:
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    Type
    Article
    Authors
    Ou, Guangjin
    Yu, Guoxian cc
    Domeniconi, Carlotta
    Lu, Xuequan
    Zhang, Xiangliang cc
    KAUST Department
    Computer Science Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Machine Intelligence & kNowledge Engineering Lab
    Date
    2020-09-21
    Online Publication Date
    2020-09-21
    Print Publication Date
    2020-12
    Embargo End Date
    2022-09-22
    Submitted Date
    2020-06-19
    Permanent link to this record
    http://hdl.handle.net/10754/665385
    
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    Abstract
    The goal of zero-shot learning (ZSL) is to build a classifier that recognizes novel categories with no corresponding annotated training data. The typical routine is to transfer knowledge from seen classes to unseen ones by learning a visual-semantic embedding. Existing multi-label zero-shot learning approaches either ignore correlations among labels, suffer from large label combinations, or learn the embedding using only local or global visual features. In this paper, we propose a Graph Convolution Networks based Multi-label Zero-Shot Learning model, abbreviated as MZSL-GCN. Our model first constructs a label relation graph using label co-occurrences and compensates the absence of unseen labels in the training phase by semantic similarity. It then takes the graph and the word embedding of each seen (unseen) label as inputs to the GCN to learn the label semantic embedding, and to obtain a set of inter-dependent object classifiers. MZSL-GCN simultaneously trains another attention network to learn compatible local and global visual features of objects with respect to the classifiers, and thus makes the whole network end-to-end trainable. In addition, the use of unlabeled training data can reduce the bias toward seen labels and boost the generalization ability. Experimental results on benchmark datasets show that our MZSL-GCN competes with state-of-the-art approaches.
    Citation
    Ou, G., Yu, G., Domeniconi, C., Lu, X., & Zhang, X. (2020). Multi-label zero-shot learning with graph convolutional networks. Neural Networks, 132, 333–341. doi:10.1016/j.neunet.2020.09.010
    Sponsors
    This work was supported by National Natural Science Foundation of China (62031003, 61872300 and 62072380).
    Publisher
    Elsevier BV
    Journal
    Neural Networks
    DOI
    10.1016/j.neunet.2020.09.010
    PubMed ID
    32977278
    Additional Links
    https://linkinghub.elsevier.com/retrieve/pii/S0893608020303336
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.neunet.2020.09.010
    Scopus Count
    Collections
    Articles; Computer Science Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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