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ICDAR 2023 Competition on Born Digital Video Text Question Answering

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Document Analysis and Recognition - ICDAR 2023 (ICDAR 2023)

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Abstract

This paper presents the final results of the ICDAR 2023 Competition on Born Digital Video Text Question Answering (i.e., BDVT-QA) which contains two major task tracks: 1) End-to-End Video Text Spotting, and 2) Video Text Question Answering. BDVT-QA aims to spot texts and answer questions from born-digital videos. The proposed competition introduces a brand new dataset consisting of 1,000 video clips fully annotated with manually-designed question/answer pairs, where the answers are based on the text captions presented in the video clips. A total of 23 final submissions were received for this competition. The top-3 performances of each track are as follows: 1)T1.1 - 57.53%, T1.2 - 53.3%, T1.3 - 52.35%, and 2) T2.1 - 31.2%, T2.2 - 28.84%, T2.3 - 21.19%. We summarize the submitted methods and give a deep analysis. Besides, this paper also includes dataset descriptions, task definitions and evaluation protocols. The dataset and the final ranking of submissions are publicly available on the challenge’s official website: https://tianchi.aliyun.com/specials/promotion/ICDAR_2023_Competition_on_Born_Digital_Video_Text_QA.

Z. Yang, X. Song and S. Song—Equal Contribution.

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Acknowledgments

The authors express their gratitude to the Competition Chairs for their valuable input in organizing the competition and for their critical review of the competition report. This challenge is sponsored by Alibaba Group. This work is also supported by NSFC (62225603), NSFC (61672273) and NSFC (61832008).

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Authors and Affiliations

  1. Alibaba Group, Hangzhou, China

    Zhibo Yang, Sibo Song, Fei Huang & Cong Yao

  2. Nanjing University, Nanjing, China

    Xiaoge Song & Tong Lu

  3. Huazhong University of Science and Technology, Wuhan, China

    Zhibo Yang & Xiang Bai

  4. Institute of Automation of Chinese Academy of Sciences, Beijing, China

    Cheng-Lin Liu

Authors
  1. Zhibo Yang
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  2. Xiaoge Song
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  3. Sibo Song
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  4. Tong Lu
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  5. Xiang Bai
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  6. Cheng-Lin Liu
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  7. Fei Huang
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  8. Cong Yao
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Corresponding author

Correspondence to Zhibo Yang .

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Editors and Affiliations

  1. TU Dortmund University, Dortmund, Germany

    Gernot A. Fink

  2. Adobe, College Park, MN, USA

    Rajiv Jain

  3. Osaka Metropolitan University, Osaka, Japan

    Koichi Kise

  4. Rochester Institute of Technology, Rochester, NY, USA

    Richard Zanibbi

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© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Yang, Z. et al. (2023). ICDAR 2023 Competition on Born Digital Video Text Question Answering. In: Fink, G.A., Jain, R., Kise, K., Zanibbi, R. (eds) Document Analysis and Recognition - ICDAR 2023. ICDAR 2023. Lecture Notes in Computer Science, vol 14188. Springer, Cham. https://doi.org/10.1007/978-3-031-41679-8_30

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  • DOI: https://doi.org/10.1007/978-3-031-41679-8_30

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-41678-1

  • Online ISBN: 978-3-031-41679-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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