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arXiv · 1806.01483

JTAV: Jointly Learning Social Media Content Representation by Fusing Textual, Acoustic, and Visual Features

Hongru Liang·Haozheng Wang·Jun Wang·Shaodi You·Zhe Sun·Jin-Mao Wei·Zhenglu Yang
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Abstract

Learning social media content is the basis of many real-world applications, including information retrieval and recommendation systems, among others. In contrast with previous works that focus mainly on single modal or bi-modal learning, we propose to learn social media content by fusing jointly textual, acoustic, and visual information (JTAV). Effective strategies are proposed to extract fine-grained features of each modality, that is, attBiGRU and DCRNN. We also introduce cross-modal fusion and attentive pooling techniques to integrate multi-modal information comprehensively. Extensive experimental evaluation conducted on real-world datasets demonstrates our proposed model outperforms the state-of-the-art approaches by a large margin.

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Hongru Liang, Haozheng Wang, Jun Wang, Shaodi You, Zhe Sun, Jin-Mao Wei, Zhenglu Yang. 2018-06-05. JTAV: Jointly Learning Social Media Content Representation by Fusing Textual, Acoustic, and Visual Features. https://arxiv.org/abs/1806.01483

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