End-to-End Video Question-Answer Generation With Generator-Pretester Network
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We study a novel task, Video Question-Answer Generation (VQAG), for challenging Video Question Answering (Video QA) task in multimedia. Due to expensive data annotation costs, many widely used, large-scale Video QA datasets such as Video-QA, MSVD-QA and MSRVTT-QA are automatically annotated using Caption Question Generation (CapQG) which inputs captions instead of the video itself. As captions neither fully represent a video, nor are they always practically available, it is crucial to generate question-answer pairs based on a video via Video Question-Answer Generation (VQAG). Existing video-to-text (V2T) approaches, despite taking a video as the input, only generate a question alone. In this work, we propose a novel model Generator-Pretester Network that focuses on two components: (1) The Joint Question-Answer Generator (JQAG) which generates a question with its corresponding answer to allow Video Question “Answering” training. (2) The Pretester (PT) verifies a generated question by trying to answer it and checks the pretested answer with both the model’s proposed answer and the ground truth answer. We evaluate our system with the only two available large-scale human-annotated Video QA datasets and achieves state-of-the-art question generation performances. Furthermore, using our generated QA pairs only on the Video QA task, we can surpass some supervised baselines. As a pre-training strategy, we outperform both CapQG and transfer learning approaches when employing semi-supervised (20%) or fully supervised learning with annotated data. These experimental results suggest the novel perspectives for Video QA training.
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[128] investigated a generator and pre-tester architecture for joint QA-QG from videos, using a video encoder for 20 frames and faster R-CNN and Resnet101.
“In [128], using a newly constructed architecture of a generator, which generates a question given a video clip and an answer, and a pre-tester, which tries to answer the generated question, has been investigated for joint QA-QG from videos.”
From Automatic question generation: a review of methodologies, datasets, evaluation metrics, and applications (2023) · cites this paper as [128]
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- Authors
- Su, H.-T., Chang, C.-H., Shen, P.-W., Wang, Y.-S., Chang, Y.-L., Chang, Y.-C., Cheng, P.-J., & Hsu, W. H.
- Affiliations
- National Taiwan University (TW)
- University of Toronto (CA)
- Venue
- IEEE Transactions on Circuits and Systems for Video Technology
- Published
- 2021-11
- Volume
- 31
- Issue
- 11
- Pages
- 4497-4507
- ISSN
- 1051-8215 (ISSN-L)
- 1558-2205
- OpenAlex
- W3127165192
- Cited by
- 38
- References
- 72
- Citations by year
- 2026: 1 · 2025: 6 · 2024: 8 · 2023: 12 · 2022: 8 · 2021: 3
- FWCI
- 2.78
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Transactions on Circuits and Systems for Video Technology (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Ministry of Science and Technology, Taiwan (MOST 109-2634-F-002-032)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
- Keywords
- video question answering, question-answer pair generation
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