语义鸿沟
计算机科学
仿形(计算机编程)
前提
社会化媒体
情报检索
情态动词
模式
自然语言处理
人工智能
机器学习
万维网
图像(数学)
哲学
社会学
化学
操作系统
高分子化学
语言学
社会科学
图像检索
作者
Lin Li,Kaixi Hu,Yunpei Zheng,Jianquan Liu,Kong Aik Lee
出处
期刊:International Conference on Acoustics, Speech, and Signal Processing
日期:2021-06-06
被引量:3
标识
DOI:10.1109/icassp39728.2021.9414808
摘要
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.
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