The DOI matches its Crossref record; 44 of the 45 references it cites were found in a registry or an index.
VizWiz Grand Challenge: Answering Visual Questions from Blind People
Open accessAbstract
The study of algorithms to automatically answer visual questions currently is motivated by visual question answering (VQA) datasets constructed in artificial VQA settings. We propose VizWiz, the first goal-oriented VQA dataset arising from a natural VQA setting. VizWiz consists of over 31,000 visual questions originating from blind people who each took a picture using a mobile phone and recorded a spoken question about it, together with 10 crowdsourced answers per visual question. VizWiz differs from the many existing VQA datasets because (1) images are captured by blind photographers and so are often poor quality, (2) questions are spoken and so are more conversational, and (3) often visual questions cannot be answered. Evaluation of modern algorithms for answering visual questions and deciding if a visual question is answerable reveals that VizWiz is a challenging dataset. We introduce this dataset to encourage a larger community to develop more generalized algorithms that can assist blind people.
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“Prior work reported over 40% disagreement between crowdworkers and in-house experts on whether a photo contained privacy or quality issues [36]”
From Disability-First AI Dataset Annotation: Co-designing Stuttered Speech Annotation Guidelines with People Who Stutter (2026) · cites this paper as [36]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
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“Although academic scholars and industry corporations have emphasized the pressing need for more disability-centered datasets [20, 38, 55, 79, 97, 111, 124]”
From "It's trained by non-disabled people": Evaluating How Image Quality Affects Product Captioning with Vision-Language Models (2026) · cites this paper as [55]
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“While prior efforts of disabilityfirst datasets often focus on the data collection process [36, 47, 63, 68, 88, 98], our study represents an important step in centering disabled people and their expertise throughout the AI development pipeline.”
From Disability-First AI Dataset Annotation: Co-designing Stuttered Speech Annotation Guidelines with People Who Stutter (2026) · cites this paper as [36]
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“From analyzing VizWiz images, Gurari et al. [55] found that blind users often struggle to take high-quality photographs, and many visual questions go unanswered because images fail to capture the relevant objects [55].”
From "It's trained by non-disabled people": Evaluating How Image Quality Affects Product Captioning with Vision-Language Models (2026) · cites this paper as [55]
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Cites
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333 · cited 2×
“Following prior work [14], we first computed the average image from all images in VizWiz.”
BackgroundMethodMicrosoft COCO: Common Objects in Context
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., & Zitnick, C. L. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10602-1_48 · Cited by 45,460 · cited 4×
“0: Real [7] MSCOCO [25] Crowd workers (AMT) Typed Visual Madlibs [43] MSCOCO [25] Automatically generated (templates) ——– FM-IQA [16] MSCOCO [25] Crowd workers (Baidu) Typed KB-VQA [40] MSCOCO [25] In-house participants Typed COCO-QA [32] MSCOCO [25] Automatically generated (captions) ——– VQA v2.”
BackgroundMethodInfluentialImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380
BLEU: a method for automatic evaluation of machine translation
Papineni, K., Roukos, S., Ward, T., & Zhu, W.-J. (2001) · Proceedings of the 40th Annual Meeting on Association for Computational Linguistics - ACL '02 · doi:10.3115/1073083.1073135 · Cited by 21,344 · cited 2×
“We employ five metrics commonly used for evaluating VQA and image description algorithms: Accuracy [7], CIDEr [38], BLEU4 [29], and ME-”
MethodIndoor segmentation and support inference from rgbd images
Silberman, N., Hoiem, D., Kohli, P., & Fergus, R. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-33715-4_54 · Cited by 5,760 · cited 1×
“DAQUAR [28] NYU Depth V2 [34] Automatically generated (templates) ——– VQA v1.”
MethodVisual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations
Krishna, R., Zhu, Y., Groth, O., Johnson, J., Hata, K., Kravitz, J., Chen, S., Kalantidis, Y., Li, L.-J., Shamma, D. A., et al. (2017) · International Journal of Computer Vision · doi:10.1007/s11263-016-0981-7 · Cited by 5,107
Deep visual-semantic alignments for generating image descriptions
Karpathy, A., & Fei-Fei, L. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298932 · Cited by 4,902
CIDEr: Consensus-based image description evaluation
Vedantam, R., Zitnick, C. L., & Parikh, D. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7299087 · Cited by 4,845 · cited 2×
“We employ five metrics commonly used for evaluating VQA and image description algorithms: Accuracy [7], CIDEr [38], BLEU4 [29], and ME-”
MethodVQA: Visual Question Answering
Antol, S., Agrawal, A., Lu, J., Mitchell, M., Batra, D., Zitnick, C. L., & Parikh, D. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.279 · Cited by 4,487
Bleu: a method for automatic evaluation of machine translation
Papineni, K. (2024) · doi:10.57702/ljteqmrb · Cited by 3,668
Flagged first, then by citations
1 of the 45 references couldn’t be matched to a known paper.
Research works printed but not linked: 3
- mahendru, 2017, The promise of premise Harnessing question premises in visual question answering
- anderson, 2017, Bottom-up and top-down attention for image captioning and visual question answering
- 0, Be My Eyes
Cited by
Sorted by citations
Improved Baselines with Visual Instruction Tuning
Liu, H., Li, C., Li, Y., & Lee, Y. J. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52733.2024.02484 · Cited by 1,748
“VizWiz [19] contains 8,000 images to evaluate model’s zero-shot generalization on visual questions asked by visually impaired people.”
BackgroundMethodInfluentialFlamingo: a visual language model for few-shot learning
Alayrac, J.-B., Donahue, J., Luc, P., Miech, A., Barr, I., Hasson, Y., Lenc, K., Mensch, A., Millican, K., Reynolds, M., et al. (2022) · Advances in Neural Information Processing Systems 35 · doi:10.52202/068431-1723 · Cited by 966
Towards VQA Models That Can Read
Singh, A., Natarajan, V., Shah, M., Jiang, Y. L., Chen, X., Batra, D., Parikh, D., & Rohrbach, M. (2019) · 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/cvpr.2019.00851 · Cited by 951
“Despite collecting open-ended answers from annotators, we find that human accuracy is 85.01%, consistent with that on VQA 2.0 [10] and VizWiz [13].”
MethodResultInfluentialIntern VL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks
Chen, Z., Wu, J., Wang, W., Su, W., Chen, G., Xing, S., Zhong, M., Zhang, Q., Zhu, X., Lu, L., et al. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52733.2024.02283 · Cited by 734
Less is More: CLIPBERT for Video-and-Language Learning via Sparse Sampling
Lei, J., Li, L., Zhou, L., Gan, Z., Berg, T. L., Bansal, M., & Liu, J. (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/cvpr46437.2021.00725 · Cited by 639
“It is trained on Visual Genome [29] for object detection and attribute classification, and produces effective grid features for image VQA tasks [3, 20].”
Method
Metadata
- Authors
- Gurari, D., Li, Q., Stangl, A. J., Guo, A., Lin, C., Grauman, K., Luo, J., & Bigham, J. P.
- Affiliations
- The University of Texas at Austin (US)
- University of Science and Technology of China (CN)
- University of Colorado Boulder (US)
- Carnegie Mellon University (US)
- University of Rochester (US)
- Venue
- 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2018-06
- Pages
- 3608-3617
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2788643321
- Cited by
- 621
- References
- 45
- Citations by year
- 2026: 92 · 2025: 133 · 2024: 108 · 2023: 65 · 2022: 56 · 2021: 66 · 2020: 55 · 2019: 29 · 2018: 17
- FWCI
- 12.58
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science Foundation (1704309, 1514118, 1755593)
- Microsoft Research
- Baidu
- 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 › Image Retrieval and Classification Techniques
- Keywords
- visual question answering, blind people, assistive technology, low-quality images, answerability
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