CASIA OpenIR  > 多媒体计算与图形学团队
Learning Multimodal Taxonomy via Variational Deep Graph Embedding and Clustering
Huaiwen Zhang1,2; Quan Fang1,2; Shengsheng Qian1,2; Changsheng Xu1,2
2018-10
Conference NameACM international conference on Multimedia
Conference DateOctober 22 - 26, 2018
Conference PlaceSeoul, Republic of Korea
Abstract

Taxonomy learning is an important problem and facilitates various applications such as semantic understanding and information retrieval. Previous work for building semantic taxonomies has primarily relied on labor-intensive human contributions or focused on text-based extraction. In this paper, we investigate the problem of automatically learning multimodal taxonomies from the multimedia data on the Web. A systematic framework called Variational Deep Graph Embedding and Clustering (VDGEC) is proposed consisting of two stages as concept graph construction and taxonomy induction via variational deep graph embedding and clustering. VDGEC discovers hierarchical concept relationships by exploiting the semantic textual-visual correspondences and contextual co-occurrences in an unsupervised manner. The unstructured semantics and noisy issues of multimedia documents are carefully addressed by VDGEC for high quality taxonomy induction. We conduct extensive experiments on the real-world datasets. Experimental results demonstrate the effectiveness of the proposed framework, where VDGEC outperforms previous unsupervised approaches by a large gap.

Indexed ByEI
Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/25824
Collection多媒体计算与图形学团队
Affiliation1.National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences
2.University of Chinese Academy of Sciences
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Huaiwen Zhang,Quan Fang,Shengsheng Qian,et al. Learning Multimodal Taxonomy via Variational Deep Graph Embedding and Clustering[C],2018.
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