Presentation information

Organized Session

Organized Session » OS-12

[1H4-OS-12b] OS-12 (2)

Tue. Jun 9, 2020 3:20 PM - 5:00 PM Room H (jsai2020online-8)

関 喜史(株式会社Gunosy)、安井 翔太(株式会社サイバーエージェント)、福田 宏幸(株式会社 電通)

3:20 PM - 3:40 PM

[1H4-OS-12b-01] Television Advertisement Analysis Using Attention-based Multimodal Network

〇Li TAO1, Xueting WANG1, Tatsuya KAWAHARA2, Toshihiko YAMASAKI1 (1. Univ. of Tokyo, 2. Video Research Ltd.)

Keywords:deep neural network, television advertising, video analysis

The impression/emotion (e.g. the recognition rate, favorableness) prediction of an advertisement is important.
It is related to multimodal features including frames, sounds, as well as metadata. In this paper, we propose a
system that can utilize different models to embed different features, and apply attention mechanism to efficiently
combine those features to help predict the impressions/emotions of audience after they watch an advertisement.
Our prediction can achieve the state-of-the-art performance in real-world dataset. This system can also detailed
analyze the importance of advertisement components.

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