@inproceedings{jin-etal-2019-low,
title = "Low-Rank {HOCA}: Efficient High-Order Cross-Modal Attention for Video Captioning",
author = "Jin, Tao and
Huang, Siyu and
Li, Yingming and
Zhang, Zhongfei",
editor = "Inui, Kentaro and
Jiang, Jing and
Ng, Vincent and
Wan, Xiaojun",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-1207/",
doi = "10.18653/v1/D19-1207",
pages = "2001--2011",
abstract = "This paper addresses the challenging task of video captioning which aims to generate descriptions for video data. Recently, the attention-based encoder-decoder structures have been widely used in video captioning. In existing literature, the attention weights are often built from the information of an individual modality, while, the association relationships between multiple modalities are neglected. Motivated by this, we propose a video captioning model with High-Order Cross-Modal Attention (HOCA) where the attention weights are calculated based on the high-order correlation tensor to capture the frame-level cross-modal interaction of different modalities sufficiently. Furthermore, we novelly introduce Low-Rank HOCA which adopts tensor decomposition to reduce the extremely large space requirement of HOCA, leading to a practical and efficient implementation in real-world applications. Experimental results on two benchmark datasets, MSVD and MSR-VTT, show that Low-rank HOCA establishes a new state-of-the-art."
}
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<abstract>This paper addresses the challenging task of video captioning which aims to generate descriptions for video data. Recently, the attention-based encoder-decoder structures have been widely used in video captioning. In existing literature, the attention weights are often built from the information of an individual modality, while, the association relationships between multiple modalities are neglected. Motivated by this, we propose a video captioning model with High-Order Cross-Modal Attention (HOCA) where the attention weights are calculated based on the high-order correlation tensor to capture the frame-level cross-modal interaction of different modalities sufficiently. Furthermore, we novelly introduce Low-Rank HOCA which adopts tensor decomposition to reduce the extremely large space requirement of HOCA, leading to a practical and efficient implementation in real-world applications. Experimental results on two benchmark datasets, MSVD and MSR-VTT, show that Low-rank HOCA establishes a new state-of-the-art.</abstract>
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%0 Conference Proceedings
%T Low-Rank HOCA: Efficient High-Order Cross-Modal Attention for Video Captioning
%A Jin, Tao
%A Huang, Siyu
%A Li, Yingming
%A Zhang, Zhongfei
%Y Inui, Kentaro
%Y Jiang, Jing
%Y Ng, Vincent
%Y Wan, Xiaojun
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F jin-etal-2019-low
%X This paper addresses the challenging task of video captioning which aims to generate descriptions for video data. Recently, the attention-based encoder-decoder structures have been widely used in video captioning. In existing literature, the attention weights are often built from the information of an individual modality, while, the association relationships between multiple modalities are neglected. Motivated by this, we propose a video captioning model with High-Order Cross-Modal Attention (HOCA) where the attention weights are calculated based on the high-order correlation tensor to capture the frame-level cross-modal interaction of different modalities sufficiently. Furthermore, we novelly introduce Low-Rank HOCA which adopts tensor decomposition to reduce the extremely large space requirement of HOCA, leading to a practical and efficient implementation in real-world applications. Experimental results on two benchmark datasets, MSVD and MSR-VTT, show that Low-rank HOCA establishes a new state-of-the-art.
%R 10.18653/v1/D19-1207
%U https://aclanthology.org/D19-1207/
%U https://doi.org/10.18653/v1/D19-1207
%P 2001-2011
Markdown (Informal)
[Low-Rank HOCA: Efficient High-Order Cross-Modal Attention for Video Captioning](https://aclanthology.org/D19-1207/) (Jin et al., EMNLP-IJCNLP 2019)
- Low-Rank HOCA: Efficient High-Order Cross-Modal Attention for Video Captioning (Jin et al., EMNLP-IJCNLP 2019)
ACL
- Tao Jin, Siyu Huang, Yingming Li, and Zhongfei Zhang. 2019. Low-Rank HOCA: Efficient High-Order Cross-Modal Attention for Video Captioning. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 2001–2011, Hong Kong, China. Association for Computational Linguistics.