VQA: Visual Question Answering: www.visualqa.org
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VQA is free-form and open-ended.
“Free-form and open-ended”
From Explainable AI in medical imaging: An overview for clinical practitioners – Beyond saliency-based XAI approaches (2023) · cites this paper as [37]
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“Inspired by the progress that benchmarking brought to computer vision [1–11] and natural language processing [12–16], the robotics community has developed several benchmarks in simulation [17–30].”
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“The image-grounded dialogue task is centered around responding to questions (Antol et al., 2015; Das et al., 2017; Kottur et al., 2019) or creating natural conversations related to specific images (Mostafazadeh et al., 2017; Shuster et al., 2018; Meng et al., 2020; Zheng et al., 2021).”
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“Early VQA suites (Antol et al. 2015; Hudson et al. 2019; Y. Zhu et al. 2016) test scene understanding through question answering, while recent holistic benchmarks (Yue, Ni, et al. 2024; Yuan Liu et al. 2024; Lin Chen et al. 2024; Yue, Zheng, et al. 2025) extend this paradigm to college-level…”
- Authors
- Agrawal, A., Lu, J., Antol, S., Mitchell, M., Zitnick, C. L., Parikh, D., & Batra, D.
- Affiliations
- Virginia Tech (US)
- Microsoft (United States) (US)
- Meta (United States) (US)
- Georgia Institute of Technology (US)
- Corresponding author
- Agrawal, Aishwarya
- Venue
- International Journal of Computer Vision
- Published
- 2016-11-08
- Volume
- 123
- Issue
- 1
- Pages
- 4-31
- ISSN
- 0920-5691 (ISSN-L)
- 1573-1405
- OpenAlex
- W2563399268
- Cited by
- 570
- References
- 57
- Citations by year
- 2026: 18 · 2025: 39 · 2024: 54 · 2023: 44 · 2022: 101 · 2021: 68 · 2020: 86 · 2019: 74 · 2018: 65 · 2017: 17 · 2016: 4
- FWCI
- 36.23
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- International Journal of Computer Vision (publishedVersion)
- 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 › Advanced Neural Network Applications
- Keywords
- visual question answering, open-ended question answering, image-text understanding, visual reasoning, automated assessment, image captioning
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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