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VQA: Visual Question Answering
Abstract
We propose the task of free-form and open-ended Visual Question Answering (VQA). Given an image and a natural language question about the image, the task is to provide an accurate natural language answer. Mirroring real-world scenarios, such as helping the visually impaired, both the questions and answers are open-ended. Visual questions selectively target different areas of an image, including background details and underlying context. As a result, a system that succeeds at VQA typically needs a more detailed understanding of the image and complex reasoning than a system producing generic image captions. Moreover, VQA is amenable to automatic evaluation, since many open-ended answers contain only a few words or a closed set of answers that can be provided in a multiple-choice format. We provide a dataset containing ~0.25M images, ~0.76M questions, and ~10M answers (www.visualqa.org), and discuss the information it provides. Numerous baselines for VQA are provided and compared with human performance.
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In the citing paper
“This level of expert disagreement is in line with what is observed in other well-known machine learning benchmarks59–62.”
From Humanity's Last Exam (2025) · cites this paper as [60]
In the citing paper
“VQA models (e.g., Antol et al., 2015) are able to give a visual answer to a verbal question; for example, in response to the question “How many giraffes are drinking water?” and a given image, the network would respond by drawing bounding boxes around all the giraffes drinking from the water hole (but not the other ones, even if they are standing by the water hole).”
From Towards a Smart Bionic Eye : AI-powered artificial vision for the treatment of incurable blindness (2022) · cites this paper as Antol et al., 2015
In the citing paper
“Although VQA has traditionally been treated as a task with a single ground-truth label [4], recent work highlights the inherent subjectivity in visual information seeking, showing that blind users integrate multiple perspectives to shape their visual understanding, whether with AI tools or humans [94].”
From Disability-First AI Dataset Annotation: Co-designing Stuttered Speech Annotation Guidelines with People Who Stutter (2026) · cites this paper as [4]
In the citing paper
“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 [84]
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Velioğlu, R., & Rose, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.12975 · Cited by 116
Sunny and Dark Outside?! Improving Answer Consistency in VQA through Entailed Question Generation
Ray, A., Sikka, K., Divakaran, A., Lee, S., & Burachas, G. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1596 · Cited by 66
Look Before you Speak: Visually Contextualized Utterances
Seo, P. H., Nagrani, A., & Schmid, C. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.01660 · Cited by 57
Shin, A., Ishii, M., & Narihira, T. (2022) · International Journal of Computer Vision · doi:10.1007/s11263-021-01547-8 · Cited by 58
HAIR: Hierarchical Visual-Semantic Relational Reasoning for Video Question Answering
Liu, F., Liu, J., Wang, N., & Lu, H. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00172 · Cited by 56
Coarse-to-Fine Reasoning for Visual Question Answering
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Named Entity and Relation Extraction with Multi-Modal Retrieval
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Metadata
- Authors
- Antol, S., Agrawal, A., Lu, J., Mitchell, M., Batra, D., Zitnick, C. L., & Parikh, D.
- Affiliations
- Virginia Tech (US)
- Microsoft (United States) (US)
- Georgia Institute of Technology (US)
- Venue
- 2015 IEEE International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2015-12
- Pages
- 2425-2433
- OpenAlex
- W1933349210
- Cited by
- 4,487
- References
- 65
- Citations by year
- 2026: 260 · 2025: 519 · 2024: 532 · 2023: 560 · 2022: 518 · 2021: 620 · 2020: 485 · 2019: 432 · 2018: 293 · 2017: 183 · 2016: 76 · 2015: 9
- FWCI
- 76.70
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE/CVF International Conference on Computer Vision (ICCV) (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 › Image Retrieval and Classification Techniques
- Keywords
- visual question answering, open-ended question answering, image understanding, visual reasoning, assistive technology for visually impaired, 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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