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]
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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
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“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
SUN database: Large-scale scene recognition from abbey to zoo
Xiao, J., Hays, J., Ehinger, K. A., Oliva, A., & Torralba, A. (2010) · 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2010.5539970 · Cited by 3,283 · cited 1×
“Historically, progress in the research community on a given computer vision problem is typically preceded by a largescale, publicly-shared dataset [12, 25, 30, 33, 42].”
BackgroundCLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning
Johnson, J., Hariharan, B., van der Maaten, L., Fei-Fei, L., Zitnick, C. L., & Girshick, R. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.215 · Cited by 1,781
Thomée, B., Shamma, D. A., Friedland, G. H., Elizalde, B., Ni, K. S., Poland, D., Borth, D., & Li, L.-J. (2016) · Communications of the ACM · doi:10.1145/2812802 · Cited by 1,715
Microsoft COCO Captions: Data Collection and Evaluation Server
Chen, X., Fang, H., Lin, T.-Y., Vedantam, R., Gupta, S., Dollar, P., & Zitnick, C. L. (2015) · arXiv · doi:10.48550/arxiv.1504.00325 · Cited by 1,614 · cited 1×
“Historically, progress in the research community on a given computer vision problem is typically preceded by a largescale, publicly-shared dataset [12, 25, 30, 33, 42].”
BackgroundAndreas, J., Rohrbach, M., Darrell, T., & Klein, D. (2016) · 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.2016.12 · Cited by 1,012 · cited 9×
“0: Real [17] MSCOCO [25] Crowd workers (AMT) Typed Visual7W [45] MSCOCO [25] Crowd workers (AMT) Typed CLEVR [19] Synthetic Shapes Automatically generated (templates) ——– SHAPES [6] Synthetic Shapes Automatically generated (templates) ——– Visual Genome [23] MSCOCO [25] & YFCC100M [35] Crowd workers (AMT) Typed FVQA [3…”
BackgroundMethodInfluentialVQA: Visual Question AnsweringPreprint
Agrawal, A., Lu, J., Antol, S., Mitchell, M., Zitnick, C. L., Batra, D., & Parikh, D. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1505.00468 · Cited by 968 · cited 10×
“For example, all images were either taken from an existing vision dataset (e.g., MSCOCO [28]) or artificially constructed (e.g., Abstract Scenes [8], computer graphics [7, 21]).”
BackgroundMethodInfluentialSUN attribute database: Discovering, annotating, and recognizing scene attributes
Patterson, G., & Hays, J. H. (2012) · 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.2012.6247998 · Cited by 876
Visual7W: Grounded Question Answering in Images
Zhu, Y., Groth, O., Bernstein, M., & Fei-Fei, L. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.540 · Cited by 794 · cited 6×
“0: Real [17] MSCOCO [25] Crowd workers (AMT) Typed Visual7W [45] MSCOCO [25] Crowd workers (AMT) Typed CLEVR [19] Synthetic Shapes Automatically generated (templates) ——– SHAPES [6] Synthetic Shapes Automatically generated (templates) ——– Visual Genome [23] MSCOCO [25] & YFCC100M [35] Crowd workers (AMT) Typed FVQA [3…”
BackgroundMethodInfluentialExploring Models and Data for Image Question AnsweringPreprint
Ren, M., Kiros, R., & Zemel, R. S. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1505.02074 · Cited by 630 · cited 6×
“Over the past three years, a plethora of VQA datasets have been publicly shared to encourage a larger community to collaborate on developing algorithms that answer visual questions [6, 7, 16, 17, 19, 20, 23, 28, 32, 39, 40, 43, 45].”
BackgroundMethodInfluentialBigham, J. P., Jayant, C., Ji, H., Little, G., Miller, A. E., Miller, R. C., Tatarowicz, A., White, B., White, S., & YEH, T. P. (2010) · International Cross-Disciplinary Conference on Web Accessibility (W4A) · doi:10.1145/1805986.1806020 · Cited by 536
Self-citationFVQA: Fact-Based Visual Question Answering
Wang, P., Wu, Q., Shen, C., Dick, A., & van den Hengel, A. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2754246 · Cited by 507 · cited 6×
“0: Real [17] MSCOCO [25] Crowd workers (AMT) Typed Visual7W [45] MSCOCO [25] Crowd workers (AMT) Typed CLEVR [19] Synthetic Shapes Automatically generated (templates) ——– SHAPES [6] Synthetic Shapes Automatically generated (templates) ——– Visual Genome [23] MSCOCO [25] & YFCC100M [35] Crowd workers (AMT) Typed FVQA [3…”
BackgroundMethodInfluentialDas, A., Kottur, S., Gupta, K., Singh, A., Yadav, D., Moura, J. M. F., Parikh, D., & Batra, D. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.121 · Cited by 445 · cited 2×
“Our findings show the statistics found in the existing artificially constructed VQA datasets, nicely summarized in [13] and [20], match statistics observed in practice.”
ResultAre You Talking to a Machine? Dataset and Methods for Multilingual Image Question AnsweringPreprint
Gao, H., Mao, J., Zhou, J., Huang, Z., Wang, L., & Xu, W. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1505.05612 · Cited by 441
A Multi-World Approach to Question Answering about Real-World Scenes based on Uncertain InputPreprint
Malinowski, M., & Fritz, M. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1410.0210 · Cited by 432 · cited 7×
“Over the past three years, a plethora of VQA datasets have been publicly shared to encourage a larger community to collaborate on developing algorithms that answer visual questions [6, 7, 16, 17, 19, 20, 23, 28, 32, 39, 40, 43, 45].”
BackgroundMethodInfluentialDas, A., Kottur, S., Gupta, K., Singh, A., Jain, D., Moura, J. M. F., Parikh, D., & Batra, D. (2017) · Cited by 432
Explicit Knowledge-based Reasoning for Visual Question Answering
Wang, P., Wu, Q., Shen, C., Dick, A., & van den Hengel, A. (2017) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2017/179 · Cited by 296 · cited 7×
“Over the past three years, a plethora of VQA datasets have been publicly shared to encourage a larger community to collaborate on developing algorithms that answer visual questions [6, 7, 16, 17, 19, 20, 23, 28, 32, 39, 40, 43, 45].”
BackgroundMethodInfluentialImage Description using Visual Dependency Representations
Elliott, D., & Keller, F. (2013) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d13-1128 · Cited by 282 · cited 1×
“We evaluate with respect to four metrics: Accuracy [8], CIDEr [42], BLEU4 [32], and METEOR [16].”
MethodAn Analysis of Visual Question Answering Algorithms
Kafle, K., & Kanan, C. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.217 · Cited by 268 · cited 7×
“0: Real [17] MSCOCO [25] Crowd workers (AMT) Typed Visual7W [45] MSCOCO [25] Crowd workers (AMT) Typed CLEVR [19] Synthetic Shapes Automatically generated (templates) ——– SHAPES [6] Synthetic Shapes Automatically generated (templates) ——– Visual Genome [23] MSCOCO [25] & YFCC100M [35] Crowd workers (AMT) Typed FVQA [3…”
BackgroundMethodInfluential(2017) · doi:10.1145/2998181.2998364 · Cited by 258
Visual challenges in the everyday lives of blind people
Brady, E. L., Morris, M. R., Zhong, Y., White, S., & Bigham, J. P. (2013) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/2470654.2481291 · Cited by 252 · cited 2×
“Our work builds off previous work [8] which established a mobile phone application that supported blind people to ask over 70,000 visual questions [10] by taking a photo and asking a question about it.”
BackgroundMethodSelf-citationUnderstanding Blind People's Experiences with Computer-Generated Captions of Social Media Images
MacLeod, H., Bennett, C. L., Morris, M. R., & Cutrell, E. (2017) · Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3025453.3025814 · Cited by 200 · cited 1×
“In addition, social media platforms, such as Facebook and Twitter, provide people a way to maintain connections with friends by enabling them to identify and tag friends in posted images as well as respond to images automatically described to them [26, 41].”
BackgroundJayant, C., Ji, H., White, S., & Bigham, J. P. (2011) · International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS) · doi:10.1145/2049536.2049573 · Cited by 168 · cited 1×
“In VizWiz, ∼28% of visual questions are deemed unanswerable by crowd workers, despite the availability of several automated systems designed to guide blind photographers to improve the image focus [3], lighting [8], or composition [18, 37, 44].”
BackgroundSelf-citationShow, Ask, Attend, and Answer: A Strong Baseline For Visual Question AnsweringPreprint
Kazemi, V., & Elqursh, A. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1704.03162 · Cited by 139
VizWiz::LocateIt - enabling blind people to locate objects in their environment
Bigham, J. P., Jayant, C., Miller, A. E., White, B., & YEH, T. P. (2010) · 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops · doi:10.1109/cvprw.2010.5543821 · Cited by 137 · cited 1×
“Such VQA systems have been shown to be valuable for many daily tasks including grocery shopping [8], locating a specific object in a complex scene [9], and choosing clothes to wear [11].”
BackgroundSelf-citationPrivacy Concerns and Behaviors of People with Visual Impairments
Ahmed, T., Hoyle, R., Connelly, K., Crandall, D., & Kapadia, A. C. (2015) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/2702123.2702334 · Cited by 136 · cited 2×
“We begin our work by implementing a rigorous filtering process to remove all visual questions that could risk compromising the safety and/or privacy of any individuals associated with them, since blind people often willingly share personal information with strangers to overcome personal obstacles [5].”
BackgroundMethodCrowdsourcing subjective fashion advice using VizWiz
Burton, M. A., Brady, E. L., Brewer, R. N., Neylan, C., Bigham, J. P., & Hurst, A. K. (2012) · International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS) · doi:10.1145/2384916.2384941 · Cited by 102 · cited 3×
“Success in developing automated methods would mitigate concerns about the many undesired consequences from today’s status quo for blind people of relying on humans to answer visual questions [8, 11, 24]; e.”
BackgroundSelf-citationVizWiz: Nearly Real-time Answers to Visual Questions
Bigham, J. P., Jayant, R., Ji, H., Little, G., Millerγ, A., Miller, R. C., Miller, R., Tatarowicz, A., White, Y., White, S., et al. (2010) · Cited by 80
Self-citationVisual Madlibs: Fill in the blank Image Generation and Question AnsweringPreprint
Yu, L., Park, E., Berg, A. C., & Berg, T. L. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1506.00278 · Cited by 75 · cited 6×
“Over the past three years, a plethora of VQA datasets have been publicly shared to encourage a larger community to collaborate on developing algorithms that answer visual questions [6, 7, 16, 17, 19, 20, 23, 28, 32, 39, 40, 43, 45].”
BackgroundMethodInfluentialAnswering visual questions with conversational crowd assistants
Lasecki, W. S., Thiha, P., Zhong, Y., Brady, E. L., & Bigham, J. P. (2013) · International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS) · doi:10.1145/2513383.2517033 · Cited by 70 · cited 3×
“Success in developing automated methods would mitigate concerns about the many undesired consequences from today’s status quo for blind people of relying on humans to answer visual questions [8, 11, 24]; e.”
BackgroundSelf-citationA qualitative study to support a blind photography mobile application
Adams, D., Morales, L. M., & Kurniawan, S. (2013) · International Conference on PErvasive Technologies Related to Assistive Environments (PETRA) · doi:10.1145/2504335.2504360 · Cited by 50 · cited 1×
“For nearly a decade, blind people have been both taking pictures [4, 8] and asking questions about the pictures they take [8, 11, 24].”
BackgroundQuestion Relevance in VQA: Identifying Non-Visual And False-Premise Questions
Ray, A., Christie, G. A., Bansal, M., Batra, D., & Parikh, D. (2016) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d16-1090 · Cited by 48
An Assisted Photography Framework to Help Visually Impaired Users Properly Aim a Camera
Vázquez, M., & Steinfeld, A. M. (2014) · ACM Transactions on Computer-Human Interaction · doi:10.1145/2651380 · Cited by 46 · cited 1×
“In VizWiz, ∼28% of visual questions are deemed unanswerable by crowd workers, despite the availability of several automated systems designed to guide blind photographers to improve the image focus [3], lighting [8], or composition [18, 37, 44].”
BackgroundReal time object scanning using a mobile phone and cloud-based visual search engine
Zhong, Y., Garrigues, P. J., & Bigham, J. P. (2013) · International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS) · doi:10.1145/2513383.2513443 · Cited by 42
Self-citationQuestion Part Relevance and Editing for Cooperative and Context-Aware VQA (C2VQA)
Toor, A. S., Wechsler, H., & Nappi, M. (2017) · International Workshop on Content-Based Multimedia Indexing (CBMI) · doi:10.1145/3095713.3095718 · Cited by 11 · cited 2×
“Our work relates to recent “relevance” datasets which were artificially constructed to include irrelevant visual questions by injecting questions that are unrelated to the contents of high quality images [20, 27, 31, 36].”
BackgroundResult
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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