CASIA OpenIR  > 模式识别国家重点实验室  > 多媒体计算与图形学
Discriminative Multimodal Embedding for Event Classication
Qi,Fan1,2; Yang,Xiaoshan2,3; Zhang,Tianzhu2,3; Xu,Changsheng2,3
Source PublicationJournal of Nerual Computing
Most of the existing multimodal event classi cation methods fuse the traditional
hand-crafted features with some manually de ned weights, which may be not
suitable to the event classi cation task with large amounts of photos. Besides,
the feature extraction and event classi cation model is always performed separately,
which cannot capture the most useful features to describe the semantic
concepts of complex events. To deal with these issues, we propose a novel discriminative
multimodal embedding (DME) model for event classi cation in user
generated photos by jointly learning the representation together with the classifi er in a uni ed framework. In the proposed DME model, we can effectively
resolve the multimodal, intra-class variation and inter-class confusion challenges
by using the contrastive constraints on the multimodal event data. Extensive
experimental results on two collected datasets demonstrate the effectiveness of
the proposed DME model for event classi cation.
KeywordEvent Classi cation Multimodal Embedding
Indexed BySCI
Document Type期刊论文
Affiliation1.School of Computer and Information, Hefei University of Technoloy
2.Institute of Automation, Chinese Academy of Sciences
3.University of Chinese Academy of Sciences
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Qi,Fan,Yang,Xiaoshan,Zhang,Tianzhu,et al. Discriminative Multimodal Embedding for Event Classication[J]. Journal of Nerual Computing,2017,Volume(Issue):pp.
APA Qi,Fan,Yang,Xiaoshan,Zhang,Tianzhu,&Xu,Changsheng.(2017).Discriminative Multimodal Embedding for Event Classication.Journal of Nerual Computing,Volume(Issue),pp.
MLA Qi,Fan,et al."Discriminative Multimodal Embedding for Event Classication".Journal of Nerual Computing Volume.Issue(2017):pp.
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