research-article

Spatiotemporal-Textual Co-Attention Network for Video Question Answering

Authors:

Yongdong ZhangAuthors Info & Claims

ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), Volume 15, Issue 2s

Article No.: 53, Pages 1 - 18

https://doi.org/10.1145/3320061

Published: 19 July 2019 Publication History

Abstract

Visual Question Answering (VQA) is to provide a natural language answer for a pair of an image or video and a natural language question. Despite recent progress on VQA, existing works primarily focus on image question answering and are suboptimal for video question answering. This article presents a novel Spatiotemporal-Textual Co-Attention Network (STCA-Net) for video question answering. The STCA-Net jointly learns spatially and temporally visual attention on videos as well as textual attention on questions. It concentrates on the essential cues in both visual and textual spaces for answering question, leading to effective question-video representation. In particular, a question-guided attention network is designed to learn question-aware video representation with a spatial-temporal attention module. It concentrates the network on regions of interest within the frames of interest across the entire video. A video-guided attention network is proposed to learn video-aware question representation with a textual attention module, leading to fine-grained understanding of question. The learned video and question representations are used by an answer predictor to generate answers. Extensive experiments on two challenging datasets of video question answering, i.e., MSVD-QA and MSRVTT-QA, have shown the effectiveness of the proposed approach.

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Cited By

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https://dl.acm.org/doi/10.1016/j.jvcir.2024.104320
Wang YLiu MSong XNie L(2024)Harnessing Representative Spatial-Temporal Information for Video Question AnsweringACM Transactions on Multimedia Computing, Communications, and Applications10.1145/367539920:10(1-20)Online publication date: 5-Jul-2024
https://dl.acm.org/doi/10.1145/3675399
Zhang QChang CSu MChang HRoy D(2024)HMTV: hierarchical multimodal transformer for video highlight query on baseballMultimedia Systems10.1007/s00530-024-01479-630:5Online publication date: 23-Sep-2024
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Index Terms

Spatiotemporal-Textual Co-Attention Network for Video Question Answering
1. Information systems
  1. Information retrieval

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Published In

cover image ACM Transactions on Multimedia Computing, Communications, and Applications

ACM Transactions on Multimedia Computing, Communications, and Applications Volume 15, Issue 2s

Special Section on Cross-Media Analysis for Visual Question Answering, Special Section on Big Data, Machine Learning and AI Technologies for Art and Design and Special Section on MMSys/NOSSDAV 2018

April 2019

381 pages

ISSN:1551-6857

EISSN:1551-6865

DOI:10.1145/3343360

Editor:
Alberto Del Bimbo
University of Firenze, Italy

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Copyright © 2019 ACM.

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Association for Computing Machinery

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Publication History

Published: 19 July 2019

Accepted: 01 March 2019

Revised: 01 March 2019

Received: 01 August 2018

Published in TOMM Volume 15, Issue 2s

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Cited By

P.J. JKovoor B(2025)Video Question AnsweringJournal of Visual Communication and Image Representation10.1016/j.jvcir.2024.104320105:COnline publication date: 11-Feb-2025
https://dl.acm.org/doi/10.1016/j.jvcir.2024.104320
Wang YLiu MSong XNie L(2024)Harnessing Representative Spatial-Temporal Information for Video Question AnsweringACM Transactions on Multimedia Computing, Communications, and Applications10.1145/367539920:10(1-20)Online publication date: 5-Jul-2024
https://dl.acm.org/doi/10.1145/3675399
Zhang QChang CSu MChang HRoy D(2024)HMTV: hierarchical multimodal transformer for video highlight query on baseballMultimedia Systems10.1007/s00530-024-01479-630:5Online publication date: 23-Sep-2024
https://dl.acm.org/doi/10.1007/s00530-024-01479-6
Xu FZhong ZZhu YZhou YLi G(2024)Appearance-Motion Dual-Stream Heterogeneous Network for VideoQAMultiMedia Modeling10.1007/978-3-031-53311-2_16(212-227)Online publication date: 29-Jan-2024
https://dl.acm.org/doi/10.1007/978-3-031-53311-2_16
Lee JHa SKang J(2023)Video Question Answering with Overcoming Spatial and Temporal Redundancy in Feature ExtractionJOURNAL OF BROADCAST ENGINEERING10.5909/JBE.2023.28.7.84928:7(849-858)Online publication date: 31-Dec-2023
https://doi.org/10.5909/JBE.2023.28.7.849
Zhao CLi BGuo K(2023)Adaptive enhancement design of non-significant regions of a Wushu action 3D image based on the symmetric difference algorithmMathematical Biosciences and Engineering10.3934/mbe.202366220:8(14793-14810)Online publication date: 2023
https://doi.org/10.3934/mbe.2023662
Peng MShao XShi YZhou X(2023)Hierarchical Synergy-Enhanced Multimodal Relational Network for Video Question AnsweringACM Transactions on Multimedia Computing, Communications, and Applications10.1145/363010120:4(1-22)Online publication date: 11-Dec-2023
https://dl.acm.org/doi/10.1145/3630101
Wang YLi PSi QZhang HZang WLin ZFu P(2023)Cross-modality Multiple Relations Learning for Knowledge-based Visual Question AnsweringACM Transactions on Multimedia Computing, Communications, and Applications10.1145/361830120:3(1-22)Online publication date: 23-Oct-2023
https://dl.acm.org/doi/10.1145/3618301
Hao JSun HRen PZhong YWang JQi QLiao J(2023)Fine-Grained Text-to-Video Temporal Grounding from Coarse BoundaryACM Transactions on Multimedia Computing, Communications, and Applications10.1145/357982519:5(1-21)Online publication date: 16-Mar-2023
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Dong SNiu TLuo XLiu WXu X(2023)Semantic Embedding Guided Attention with Explicit Visual Feature Fusion for Video CaptioningACM Transactions on Multimedia Computing, Communications, and Applications10.1145/355027619:2(1-18)Online publication date: 6-Feb-2023
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