The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.
"Person, Shoes, Tree. Is the Person Naked?" What People with Vision Impairments Want in Image Descriptions
Open accessAbstract
Access to digital images is important to people who are blind or have low vision (BLV). Many contemporary image description efforts do not take into account this population's nuanced image description preferences. In this paper, we present a qualitative study that provides insight into 28 BLV people's experiences with descriptions of digital images from news websites, social networking sites/platforms, eCommerce websites, employment websites, online dating websites/platforms, productivity applications, and e-publications. Our findings reveal how image description preferences vary based on the source where digital images are encountered and the surrounding context. We provide recommendations for the development of next-generation image description technologies inspired by our empirical analysis.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The text we found for this paper looks like another document, so the self-check wasn’t run.
Citations
Cites
Microsoft 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
Proceedings of IEEE Conference on Computer Vision and Pattern Recognition
(1993) · doi:10.1109/cvpr.1993 · Cited by 17,989
Show and tell: A neural image caption generator
Vinyals, O., Toshev, A., Bengio, S., & Erhan, D. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298935 · Cited by 6,318
From captions to visual concepts and back
Fang, H., Gupta, S., Iandola, F., Srivastava, R. K., Deng, L., Dollár, P., Gao, J., He, X., Mitchell, M., Platt, J. C., et al. (2015) · 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.2015.7298754 · Cited by 1,321
VizWiz: nearly real-time answers to visual questions
Bigham, J. P., Jayant, C., Ji, H., Little, G., Miller, A., Miller, R. C., Miller, R., Tatarowicz, A., White, B., White, S., et al. (2010) · Proceedings of the 23nd annual ACM symposium on User interface software and technology · doi:10.1145/1866029.1866080 · Cited by 601
(2017) · doi:10.1145/2998181.2998364 · Cited by 258
Understanding 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
Self-citationHow Blind People Interact with Visual Content on Social Networking Services
Voykinska, V., Azenkot, S., Wu, S., & Leshed, G. (2016) · ACM Conference on Computer Supported Cooperative Work (CSCW) · doi:10.1145/2818048.2820013 · Cited by 190
How many participants are really enough for usability studies?
Alroobaea, R., & Mayhew, P. J. (2014) · Science and Information Conference · doi:10.1109/sai.2014.6918171 · Cited by 184
Research works printed but not linked: 9
- Virginia Braun and Victoria Clarke. 2006. Using thematic analysis in psychology. Qualitative research in psychology 3, 2 (2006), 77--101.
- Cole Gleason, Patrick Carrington, Cameron Cassidy, Meredith Ringel Morris, Kris M. Kitani, and Jeffrey P. Bigham. 2019. &Ldquo;It's Almost Like They'Re Trying to Hide It&Rdquo;: How User-Provided Image Descriptions Have Failed to Make Twitter Accessible. In The World Wide Web Conference (WWW '19). ACM, NY, NY, USA, 549--559.
- Danna Gurari, Qing Li, Abigale J. Stangl, Anhong Guo, Chi Lin, Kristen Grauman, Jiebo Luo, and Jeffrey P. Bigham. 2018. VizWiz Grand Challenge: Answering Visual Questions From Blind People. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
- AI Now Institute. 2019. Disability, Bias, and AI. https://ainowinstitute.org/disabilitybiasai-2019.pdf. (November 2019). (Accessed on 01/02/2020).
- Os Keyes and Cynthia L. Bennett. 2019. What Is the Point of Fairness? Disability, AI and The Complexity of Justice. arXiv preprint arXiv:1908.01024 (2019). · arXiv
- Jonathan Lazar, Alfreda Dudley-Sponaugle, and Kisha-Dawn Greenidge. 2004. Improving web accessibility: a study of webmaster perceptions. Computers in human behavior 20, 2 (2004), 269--288.
- Meredith Ringel Morris. 2019. AI and Accessibility: A Discussion of Ethical Considerations. arXiv preprint arXiv:1908.08939 (2019). · arXiv
- Helen Petrie, Chandra Harrison, and Sundeep Dev. 2005. Describing images on the web: a survey of current practice and prospects for the future. Proceedings of Human Computer Interaction International (HCII) 71 (2005).
- Anselm Strauss and Juliet Corbin. 1998. Basics of qualitative research techniques. Sage publications Thousand Oaks, CA.
Cited by
Sorted by citations
Items 101–140 of 140
Jung, C., Mehta, S., Kulkarni, A., Zhao, Y., & Kim, Y.-S. (2022) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2021.3114846 · Cited by 91
Improving Image Accessibility by Combining Haptic and Auditory Feedback
Alkhathlan, M., Tlachac, M., Harrison, L., & Rundensteiner, E. A. (2022) · International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS) · doi:10.1145/3517428.3550362 · Cited by 3
“Unfortunately, most image content is largely inaccessible [1, 7, 24] to the more than 1.”
BackgroundRepresentation in Media Accessibility
Iturregui-Gallardo, G. (2023) · Journal of Audiovisual Translation · doi:10.47476/jat.v6i1.2023.257 · Cited by 4
“Based on works on disability and representation (Bennett et al., 2021; Stangl et al., 2020), the article goes on to discuss models of representation in the media (Clark, 1969; Hall, 2018; Casey et al., 2007).”
BackgroundNetizen A11y: Engaging Internet Users in Making Visual Media Accessible
Wang, R. (2024) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3640544.3645247 · Cited by 2
“However, prior studies [13, 15] show that BLV people still face significant barriers in efficiently seeking required information from online shopping websites due to inadequate image descriptions and screen readers’ inability to effectively filter out a large amount of information on a product page.”
BackgroundNew to universal design for learning? Start here!
Houghton, J. (2022) · New Directions for Teaching and Learning · doi:10.1002/tl.20525 · Cited by 2
Reference-Based Metrics Are Biased Against Blind and Low-Vision Users’ Image Description Preferences
Kapur, R., & Kreiss, E. (2024) · Proceedings of the Third Workshop on NLP for Positive Impact · doi:10.18653/v1/2024.nlp4pi-1.26 · Cited by 2
“While BLV user preferences for image descriptions are usually studied in isolation (Das et al., 2024; Muehlbradt and Kane, 2022; Stangl et al., 2020), recent work has started to investigate how those preferences compare to sighted user judgments (Kreiss et al., 2022a; Lundgard and Satyanarayan,…”
BackgroundAIDE: an automatic image description engine for review imagery
Sreedhar, R., Tan, N., Zhang, J., Jin, K., Gregson, S., Moreta-Feliz, E., Samudrala, N., & Sadalgi, S. (2022) · Proceedings of the 19th International Web for All Conference · doi:10.1145/3493612.3520465 · Cited by 1
Improving Automatic Image Annotation System to handle Spatial Analysis of Aerial Imagery
Al-Shammary, A. A., Zaghden, N., & Bouhlel, M. S. (2022) · International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT) · doi:10.1109/ismsit56059.2022.9932772 · Cited by 1
Developing and Pilot Testing a Hybrid Prototype for Audio-Tactile Graphics
Rassmus-Gröhn, K., Saeed, T., Magnusson, C., Claassen, G. B., Mathé, L., & Schulze-Velmede, T. (2025) · Springer eBooks · doi:10.1007/978-3-032-01628-7_53 · Cited by 1
Tanaka, K., Tsutsui, A., & Ochiai, Y. (2025) · International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS) · doi:10.1145/3663547.3759740 · Cited by 1
Early Design Stages of a Refreshable Pin Display: User Involvement and Design Recommendations
Saeed, T., Magnusson, C., Rassmus-Gröhn, K., Panëels, S., Mathé, L., Zimmerman, T., Galcera, A., Claassen, G. B., Balčikonis, V., & Pantera, L. (2026) · Signals and communication technology · doi:10.1007/978-3-031-91550-5_19 · Cited by 1
Show, Prefer and Tell: Incorporating User Preferences into Image Captioning
Lindh, A., Ross, R., & Kelleher, J. D. (2023) · ACM Symposium on Applied Computing · doi:10.1145/3555776.3577794 · Cited by 1
Saini, M., & Sengupta, E. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-20159-1 · Cited by 1
“If BVIP is aware of the information included in social media photographs, he may engage fully in online discussions while taking in news and enjoyment [67].”
Barriers and Opportunities to Accessible Social Media Content AuthoringPreprint
Pereira, L. S., Coelho, J., Rodrigues, A., Guerreiro, J., Guerreiro, T., & Duarte, C. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.10968 · Cited by 1
Experiences and Perceptions of Blind and Low-Vision (BLV) People with Retail Self-Checkout Systems
Heisey, A., Shinohara, K., & Tigwell, G. W. (2026) · ACM Transactions on Accessible Computing · doi:10.1145/3816243
“Computing research has sought to address this by extracting details from product web pages [87, 88, 98].”
On the use of human reference data for evaluating automatic image descriptionsPreprint
van Miltenburg, E. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.08792
“Although the development of image description guidelines is an active area of research (see [9] for an overview), there is a disconnect be-tween the human-computer interaction (HCI) literature and the image captioning (IC) literature.”
BackgroundHaptic Auditory Feedback for Enhanced Image Description: A Study of User Preferences and Performance
Alkhathlan, M., Tlachac, M., & Rundensteiner, E. A. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-42280-5_14 · Cited by 1
The Current Landscape of Audio Description Research: A Survey
Rajiv, L. G. A. (2023) · International Journal of Science and Research (IJSR) · doi:10.21275/sr23209131618
Through the Eyes of Instagram: Analyzing Image Content utilizing Meta's Automatic Alt-Text
Olivetti, J. F. H., & Melo, P. D. F. (2024) · Brazilian Symposium on Multimedia and the Web · doi:10.5753/webmedia.2024.241695
Reiter, E. (2024) · Natural Language Generation · doi:10.1007/978-3-031-68582-8_4
Bridging the Accessibility Gap in Online Shopping with AI - Driven Solutions
Wroblewski, C., Mirza, A. B., & Lin, W. (2024) · International Conference on Computer and Applications (ICCA) · doi:10.1109/icca62237.2024.10927778
“Furthermore, BLV users have diverse preferences, favoring descriptions that are specific to the product type they are browsing [7].”
Keller: Understanding the Experience of Low Vision Users with an Image to Text Generator Extension
Rey, P. A. R., Canlas, S. A. C., Solamo, M. R., Feria, R. P., & Figueroa, L. L. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-032-06634-3_24
VisionAidQA: Advancing Visual Question Answering for the Visually Impaired
Pal, R., Kar, S., Prasad, D. K., & Sekh, A. A. (2026) · ACM Computing Surveys · doi:10.1145/3788861
“These VQA systems have proven helpful for VI people in a variety of daily tasks, such as online/offline shopping [77, 91], finding a specific object in a complicated scene [122], and making wardrobe decisions from customers’ reviews [111], among others.”
Walking the Walk: Centering Students with Disabilities in Accessibility Education
Elglaly, Y. N., Engebretson, D. C., Leaman, J., & Howard, E. (2026) · ACM Technical Symposium on Computer Science Education (SIGCSE) · doi:10.1145/3770762.3772639
“For example, after reading a study on what blind and visually impaired users want in image descriptions [41], students were not asked to simply summarize the findings.”
Data Fidget: Designing a Tactile Companion for and with Blind and Low Vision Data Readers
Burzio, G., Szabó, Á., Karyda, M., & Ferraro, V. (2026) · International Conference on Tangible, Embedded, and Embodied Interaction (TEI) · doi:10.1145/3731459.3779325
“The major issue is that not all non-text elements are designed in an accessible way and embedding alternative texts [21].”
Ryskeldiev, B., Gillingham, M., & Kobori, N. (2026) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3772363.3799042
Botero, V. B., Sharma, S., Xu, R. I., DiSalvo, L. M., Sharma, R., & Smith, B. A. (2026) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3772363.3799011
Access to Interpretation: How Formal Cues Ground Interpretive Alt Text for Paintings
Zhong, V., Jiang, L., & Ringland, K. E. (2026) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3772363.3798632
Lyu, Y., Shen, J. P., Faisal, A., & Carroll, J. M. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790337 · Cited by 5
“Prior research has shown that visually impaired people are heavy users of social media [65, 94].”
Lyu, Y., Dillahunt, T. R., Liu, J., & Carroll, J. M. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790567 · Cited by 1
“Stangl et al. [88] examined how people with visual impairments interpret images depending on the platform: on general social networks, they cared about who was in the picture and what they were doing; on dating platforms, they focused more on appearance-related attributes such as hairstyle, body…”
Generating clothing graphic captions for visually impaired users
Alluqmani, A., Harvey, M., & Paramita, M. (2026) · Multimedia Tools and Applications · doi:10.1007/s11042-026-21774-w
Salous, M., Woźniak, M. P., von Reeken, T., Kramer, M., Heuten, W., Boll, S., & Abdenebaoui, L. (2026) · International Conference on Conversational User Interfaces · doi:10.1145/3816046.3816207 · Cited by 1
“Prompts should instruct the model to include, when visually grounded: (i) People : number of people, apparent roles/interaction, clothing, salient accessories/objects-in-use, and facial expression or clear affective cues; identity should not be inferred beyond visible evidence [45, 46].”
Lee, Y., Moon, S., Joh, H., Park, S., & Oh, U. (2026) · Universal Access in the Information Society · doi:10.1007/s10209-026-01369-w
A Born-Accessible Approach to Technology Design
Lazar, J. K., Shachmut, K., Drummond, A., Jay, W., Rosenblum, H., & Eng, K. (2026) · interactions · doi:10.1145/3830007
Ko, T., & Lee, K. (2026) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3817018
Making Videos Accessible for Blind and Low Vision Users Using a Multimodal Agent Video Player
Sinha, A. K., Olmos, A., Santos, R. D., Rodriguez, R. R., Landay, J. A., Sepah, S., Nelson, P., & Kane, S. K. (2026) · Companion Publication of the International Conference on Multimodal Interaction (ICMI Companion) · doi:10.1145/3776591.3833853
“…that includes the ability to interpret scenes across multiple frames, which is helpful for insights into video content, and a way to use the MLLM to create a dense index of the entire video for high accuracy, interactive dialogue, with a retrieval augmented generation (RAG) approach [35].”
Turkarslan, A., Thakkar, V., Tekle, R., Chen*, T., Martinez, J. J., & Mankoff, J. C. (2026) · International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS) · doi:10.1145/3797867.3829048
“Regardless of image type, many BLV participants in relevant studies emphasize the importance of the context an image lives within and its overall purpose in a file [30, 39].”
Song, H., Shin, M., Choi, J., Kim, Y., Jang, K., Suh, B., & Jung, H. (2026) · Proceedings of the 2026 28th International ACM SIGACCESS Conference on Computers and Accessibility · doi:10.1145/3797867.3828985
“Prior research has established that BLVUs’ description needs vary across domains [46, 47] and has advanced generation through context-aware [17] and guideline-grounded [44] approaches.”
InfluentialSynthesizing and Evaluating a Visual Feedback Framework for Accessible Content Creation
Zhang, L., Hua, A., & Findlater, L. (2026) · International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS) · doi:10.1145/3797867.3829025
“While existing visual description guidelines (e.g., [3, 16, 17, 65]) provide relevant insights—such as contextual descriptions [27, 41, 59, 60], on-demand access to details [42, 44, 74], logically structured feedback [62, 66], objective reporting [3, 16, 17], and multimodal approaches [10, 20,…”
Metadata
- Authors
- Stangl, A., Morris, M. R., & Gurari, D.
- Affiliations
- The University of Texas at Austin (US)
- Microsoft (United States) (US)
- Venue
- Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems
- Publisher
- ACM
- Published
- 2020-04-21
- Pages
- 1-13
- OpenAlex
- W2998746484
- Cited by
- 139
- References
- 31
- Citations by year
- 2026: 19 · 2025: 15 · 2024: 25 · 2023: 22 · 2022: 28 · 2021: 22 · 2020: 8
- FWCI
- 27.00
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- CHI Conference on Human Factors in Computing Systems (publishedVersion)
- Funders
- Micorsoft
- Topics
- Social Sciences › Human Factors and Ergonomics › Digital Accessibility for Disabilities
- Health Sciences › Health Professions › Occupational Therapy › Assistive Technology in Communication and Mobility
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Keywords
- picture description, blind and visually impaired users, accessibility, digital images, web accessibility, digital platforms
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 ›
Are you an author, or do you see a problem with this page? Request a review
