Video Question Answering with Spatio-Temporal Reasoning
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The most common existing VQA methods use attention and memory mechanisms to learn visual artifacts and the semantic correlations needed to answer questions accurately.
“Currently, the most common existing methods use attention [125, 127, 129] and memory [126] mechanisms to efficiently learn visual artifacts and the semantic correlations that allow questions to be answered accurately.”
From A survey on deep multimodal learning for computer vision: advances, trends, applications, and datasets (2022) · cites this paper as [129]
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Bayoudh, K., Knani, R., Hamdaoui, F., & Mtibaa, A. (2022) · The Visual Computer · doi:10.1007/s00371-021-02166-7 · Cited by 456 · see citing works
ScanQA: 3D Question Answering for Spatial Scene Understanding
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- Authors
- Jang, Y., Song, Y., Kim, C. D., Yu, Y., Kim, Y., & Kim, G.
- Affiliations
- Seoul National University (KR)
- Microsoft (United States) (US)
- Corresponding author
- Kim, Gunhee
- Venue
- International Journal of Computer Vision
- Published
- 2019-06-18
- Volume
- 127
- Issue
- 10
- Pages
- 1385-1412
- ISSN
- 0920-5691 (ISSN-L)
- 1573-1405
- OpenAlex
- W2951161814
- Cited by
- 46
- References
- 68
- Citations by year
- 2026: 1 · 2025: 10 · 2024: 8 · 2023: 8 · 2022: 11 · 2021: 6 · 2020: 2
- FWCI
- 2.13
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- International Journal of Computer Vision (publishedVersion)
- Seoul National University Open Repository (Seoul National University) (submittedVersion)
- Funders
- IITP (2017-0-01772)
- Yahoo Research (Academic Research Program)
- Seoul National University (Creative-Pioneering Researchers Program)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Human Pose and Action Recognition
- Keywords
- video question answering, spatio-temporal reasoning, visual question answering, spatial attention, temporal attention, video understanding
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