Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering
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Most currently available datasets for training question generation systems are crowd-sourced, which largely affects the quality of generated questions.
“Most datasets that are currently available for training question generation systems are crowd-sourced [42, 47, 84, 92, 114, 115, 119, 120, 122, 123], which largely impacts the quality of generated questions.”
From Automatic question generation: a review of methodologies, datasets, evaluation metrics, and applications (2023) · cites this paper as [119]
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- Authors
- Goyal, Y., Khot, T., Agrawal, A., Summers-Stay, D., Batra, D., & Parikh, D.
- Affiliations
- Georgia Institute of Technology (US)
- Carnegie Mellon University (US)
- Corresponding author
- Goyal, Yash
- Venue
- International Journal of Computer Vision
- Published
- 2018-09-11
- Volume
- 127
- Issue
- 4
- Pages
- 398-414
- ISSN
- 0920-5691 (ISSN-L)
- 1573-1405
- OpenAlex
- W2952228917
- Cited by
- 487
- References
- 48
- Citations by year
- 2026: 19 · 2025: 27 · 2024: 33 · 2023: 105 · 2022: 79 · 2021: 91 · 2020: 62 · 2019: 48 · 2018: 18 · 2017: 5
- FWCI
- 27.38
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- International Journal of Computer Vision (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science Foundation (CAREER)
- Nvidia
- Office of Naval Research (N00014-14-1-0679, N00014)
- Army Research Office
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
- Keywords
- visual question answering, image understanding, language priors, data bias, interpretable models, trust in AI
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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