2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDMMSP-7.4
Paper Title COOPNET: MULTI-MODAL COOPERATIVE GENDER PREDICTION IN SOCIAL MEDIA USER PROFILING
Authors Lin Li, Kaixi Hu, Yunpei Zheng, Wuhan University of Technology, China; Jianquan Liu, NEC Corporation, Japan; Kong Aik Lee, Agency for Science, Technology and Research ‎(A*STAR)‎, Singapore
SessionMMSP-7: Multimodal Perception, Integration and Multisensory Fusion
LocationGather.Town
Session Time:Friday, 11 June, 13:00 - 13:45
Presentation Time:Friday, 11 June, 13:00 - 13:45
Presentation Poster
Topic Multimedia Signal Processing: Human Centric Multimedia
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract The principal way of performing user profiling is to investigate accumulated social media data. However, the problem of information asymmetry generally exists in user generated contents since users post multi-modal contents in social media freely. In this paper, we propose a novel text-image cooperation framework (COOPNet), a bridge connection network architecture that exchanges information between texts and images. First, we map the representations of both visual and sentiment enriched textual modalities into a cooperative semantic space to derive a cooperative representation. Next, the representations of texts and images are combined with their cooperative representation to exchange knowledge in the learning process. Finally, a multi-modal regression is leveraged to make cooperative decisions. Extensive experiments on the public PAN-2018 dataset demonstrate the efficacy of our framework over the state-of-the-art methods on the premise of automatic feature learning.