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        <title>Watch, Listen, and Describe: Globally and Locally Aligned Cross-Modal Attentions for Video Captioning</title>
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    <abstract>A major challenge for video captioning is to combine audio and visual cues. Existing multi-modal fusion methods have shown encouraging results in video understanding. However, the temporal structures of multiple modalities at different granularities are rarely explored, and how to selectively fuse the multi-modal representations at different levels of details remains uncharted. In this paper, we propose a novel hierarchically aligned cross-modal attention (HACA) framework to learn and selectively fuse both global and local temporal dynamics of different modalities. Furthermore, for the first time, we validate the superior performance of the deep audio features on the video captioning task. Finally, our HACA model significantly outperforms the previous best systems and achieves new state-of-the-art results on the widely used MSR-VTT dataset.</abstract>
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%0 Conference Proceedings
%T Watch, Listen, and Describe: Globally and Locally Aligned Cross-Modal Attentions for Video Captioning
%A Wang, Xin
%A Wang, Yuan-Fang
%A Wang, William Yang
%Y Walker, Marilyn
%Y Ji, Heng
%Y Stent, Amanda
%S Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F wang-etal-2018-watch
%X A major challenge for video captioning is to combine audio and visual cues. Existing multi-modal fusion methods have shown encouraging results in video understanding. However, the temporal structures of multiple modalities at different granularities are rarely explored, and how to selectively fuse the multi-modal representations at different levels of details remains uncharted. In this paper, we propose a novel hierarchically aligned cross-modal attention (HACA) framework to learn and selectively fuse both global and local temporal dynamics of different modalities. Furthermore, for the first time, we validate the superior performance of the deep audio features on the video captioning task. Finally, our HACA model significantly outperforms the previous best systems and achieves new state-of-the-art results on the widely used MSR-VTT dataset.
%R 10.18653/v1/N18-2125
%U https://aclanthology.org/N18-2125
%U https://doi.org/10.18653/v1/N18-2125
%P 795-801

Markdown (Informal)

[Watch, Listen, and Describe: Globally and Locally Aligned Cross-Modal Attentions for Video Captioning](https://aclanthology.org/N18-2125) (Wang et al., NAACL 2018)

• Watch, Listen, and Describe: Globally and Locally Aligned Cross-Modal Attentions for Video Captioning (Wang et al., NAACL 2018)

ACL

• Xin Wang, Yuan-Fang Wang, and William Yang Wang. 2018. Watch, Listen, and Describe: Globally and Locally Aligned Cross-Modal Attentions for Video Captioning. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), pages 795–801, New Orleans, Louisiana. Association for Computational Linguistics.