著者

長崎 好輝 林 昌希 金子 直史 青木 義満

出版者

公益社団法人 精密工学会

雑誌

精密工学会誌 (ISSN:09120289)

巻号頁・発行日

vol.88, no.3, pp.263-268, 2022-03-05 (Released:2022-03-05)

参考文献数

10

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In this paper, we propose a new method for audio-visual event localization 1) to find the corresponding segment between audio and visual event. While previous methods use Long Short-Term Memory (LSTM) networks to extract temporal features, recurrent neural networks like LSTM are not able to precisely learn long-term features. Thus, we propose a Temporal Cross-Modal Attention (TCMA) module, which extract temporal features more precisely from the two modalities. Inspired by the success of attention modules in capturing long-term features, we introduce TCMA, which incorporates self-attention. Finally, we were able to localize audio-visual event precisely and achieved a higher accuracy than the previous works.