Captioning Images Taken by People Who Are Blind

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12362)


While an important problem in the vision community is to design algorithms that can automatically caption images, few publicly-available datasets for algorithm development directly address the interests of real users. Observing that people who are blind have relied on (human-based) image captioning services to learn about images they take for nearly a decade, we introduce the first image captioning dataset to represent this real use case. This new dataset, which we call VizWiz-Captions, consists of over 39,000 images originating from people who are blind that are each paired with five captions. We analyze this dataset to (1) characterize the typical captions, (2) characterize the diversity of content found in the images, and (3) compare its content to that found in eight popular vision datasets. We also analyze modern image captioning algorithms to identify what makes this new dataset challenging for the vision community. We publicly-share the dataset with captioning challenge instructions at



We thank Meredith Ringel Morris, Ed Cutrell, Neel Joshi, Besmira Nushi, and Kenneth R. Fleischmann for their valuable discussions about this work. We thank Peter Anderson and Harsh Agrawal for sharing their code for setting up the EvalAI evaluation server. We thank the anonymous crowdworkers for providing the annotations. This work is supported by National Science Foundation funding (IIS-1755593), gifts from Microsoft, and gifts from Amazon.

Supplementary material

504472_1_En_25_MOESM1_ESM.pdf (27.1 mb)
Supplementary material 1 (pdf 27800 KB)


  1. 1.
  2. 2.
  3. 3.
    Home - Aira: Aira.
  4. 4.
    How does automatic alt text work on Facebook? — Facebook Help Center.
  5. 5.
    TapTapSee - Blind and Visually Impaired Assistive Technology - powered by the Image Recognition API.
  6. 6.
    Agrawal, H., et al.: Nocaps: novel object captioning at scale. arXiv preprint arXiv:1812.08658 (2018)
  7. 7.
    Anderson, P., Fernando, B., Johnson, M., Gould, S.: SPICE: semantic propositional image caption evaluation. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9909, pp. 382–398. Springer, Cham (2016). Scholar
  8. 8.
    Anderson, P., et al.: Bottom-up and top-down attention for image captioning and visual question answering. In: CVPR (2018)Google Scholar
  9. 9.
    Bai, S., An, S.: A survey on automatic image caption generation. Neurocomputing 311, 291–304 (2018)CrossRefGoogle Scholar
  10. 10.
    Bennett, C.L., Mott, M.E., Cutrell, E., Morris, M.R.: How teens with visual impairments take, edit, and share photos on social media. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, p. 76. ACM (2018)Google Scholar
  11. 11.
    Bigham, J.P., et al.: VizWiz: nearly real-time answers to visual questions. In: Proceedings of the 23rd Annual ACM Symposium on User Interface Software and Technology, pp. 333–342. ACM (2010)Google Scholar
  12. 12.
    Brady, E., Morris, M.R., Zhong, Y., White, S., Bigham, J.P.: Visual challenges in the everyday lives of blind people. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 2117–2126. ACM (2013)Google Scholar
  13. 13.
    Burton, M.A., Brady, E., Brewer, R., Neylan, C., Bigham, J.P., Hurst, A.: Crowdsourcing subjective fashion advice using VizWiz: challenges and opportunities. In: Proceedings of the 14th International ACM SIGACCESS Conference on Computers and Accessibility, pp. 135–142. ACM (2012)Google Scholar
  14. 14.
    Chen, J., Kuznetsova, P., Warren, D., Choi, Y.: Déja image-captions: a corpus of expressive descriptions in repetition. In: Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 504–514 (2015)Google Scholar
  15. 15.
    Chen, X., et al.: Microsoft COCO captions: data collection and evaluation server. arXiv preprint arXiv:1504.00325 (2015)
  16. 16.
    Chiu, T.-Y., Zhao, Y., Gurari, D.: Assessing image quality issues for real-world problems. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3646–3656 (2020)Google Scholar
  17. 17.
    Denkowski, M., Lavie, A.: Meteor universal: language specific translation evaluation for any target language. In: Proceedings of the EACL 2014 Workshop on Statistical Machine Translation (2014)Google Scholar
  18. 18.
    Elliott, D., Keller, F.: Image description using visual dependency representations. In: Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pp. 1292–1302 (2013)Google Scholar
  19. 19.
    Farhadi, A., et al.: Every picture tells a story: generating sentences from images. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6314, pp. 15–29. Springer, Heidelberg (2010). Scholar
  20. 20.
    Feng, Y., Lapata, M.: Automatic image annotation using auxiliary text information. In: Proceedings of ACL 2008: HLT, pp. 272–280 (2008)Google Scholar
  21. 21.
    Gan, C., Gan, Z., He, X., Gao, J., Deng, L.: StyleNet: generating attractive visual captions with styles. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3137–3146 (2017)Google Scholar
  22. 22.
    Grubinger, M., Clough, P., Müller, H., Deselaers, T.: The IAPR TC-12 benchmark: a new evaluation resource for visual information systems. In: International Workshop OntoImage, vol. 5 (2006)Google Scholar
  23. 23.
    Guinness, D., Cutrell, E., Morris, M.R.: Caption crawler: enabling reusable alternative text descriptions using reverse image search. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, p. 518. ACM (2018)Google Scholar
  24. 24.
    Gurari, D., et al.: VizWiz-Priv: a dataset for recognizing the presence and purpose of private visual information in images taken by blind people. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 939–948 (2019)Google Scholar
  25. 25.
    Gurari, D., et al.: VizWiz grand challenge: answering visual questions from blind people. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3608–3617 (2018)Google Scholar
  26. 26.
    Harwath, D., Glass, J.: Deep multimodal semantic embeddings for speech and images. In: 2015 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU), pp. 237–244. IEEE (2015)Google Scholar
  27. 27.
    Havard, W., Besacier, L., Rosec, O.: SPEECH-COCO: 600k visually grounded spoken captions aligned to MSCOCO data set. arXiv preprint arXiv:1707.08435 (2017)
  28. 28.
    Hodosh, M., Young, P., Hockenmaier, J.: Framing image description as a ranking task: data, models and evaluation metrics. J. Artif. Intell. Res. 47, 853–899 (2013)MathSciNetCrossRefGoogle Scholar
  29. 29.
    Hossain, M.D., Sohel, F., Shiratuddin, M.F., Laga, H.: A comprehensive survey of deep learning for image captioning. ACM Comput. Surv. (CSUR) 51(6), 118 (2019)CrossRefGoogle Scholar
  30. 30.
    Huang, L., Wang, W., Chen, J., Wei, X.-Y.: Attention on attention for image captioning. In: International Conference on Computer Vision (2019)Google Scholar
  31. 31.
    Jas, M., Parikh, D.: Image specificity. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2727–2736 (2015)Google Scholar
  32. 32.
    Kong, C., Lin, D., Bansal, M., Urtasun, R., Fidler, S.: What are you talking about? Text-to-image coreference. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3558–3565 (2014)Google Scholar
  33. 33.
    Krishna, R., et al.: Visual genome: connecting language and vision using crowdsourced dense image annotations. Int. J. Comput. Vision 123(1), 32–73 (2017). Scholar
  34. 34.
    Kulkarni, G., et al.: BabyTalk: understanding and generating simple image descriptions. IEEE Trans. Pattern Anal. Mach. Intell. 35(12), 2891–2903 (2013)CrossRefGoogle Scholar
  35. 35.
    Lin, C.-Y.: ROUGE: a package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004)Google Scholar
  36. 36.
    Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740–755. Springer, Cham (2014). Scholar
  37. 37.
    MacLeod, H., Bennett, C.L., Morris, M.R., Cutrell, E.: Understanding blind people’s experiences with computer-generated captions of social media images. In: Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, pp. 5988–5999. ACM (2017)Google Scholar
  38. 38.
    Morris, M.R., Johnson, J., Bennett, C.L., Cutrell, E.: Rich representations of visual content for screen reader users. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, p. 59. ACM (2018)Google Scholar
  39. 39.
    Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: BLEU: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, pp. 311–318. Association for Computational Linguistics (2002)Google Scholar
  40. 40.
    Patterson, G., Hays, J.: Sun attribute database: discovering, annotating, and recognizing scene attributes. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2751–2758. IEEE (2012)Google Scholar
  41. 41.
    Patterson, G., Hays, J.: COCO attributes: attributes for people, animals, and objects. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9910, pp. 85–100. Springer, Cham (2016). Scholar
  42. 42.
    Petrie, H., Harrison, C., Dev, S.: Describing images on the web: a survey of current practice and prospects for the future. In: Proceedings of Human Computer Interaction International (HCII), no. 71 (2005)Google Scholar
  43. 43.
    Rashtchian, C., Young, P., Hodosh, M., Hockenmaier, J.: Collecting image annotations using Amazon’s Mechanical Turk. In: Proceedings of the NAACL HLT 2010 Workshop on Creating Speech and Language Data with Amazon’s Mechanical Turk, pp. 139–147. Association for Computational Linguistics (2010)Google Scholar
  44. 44.
    Russakovsky, O., et al.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vision 115(3), 211–252 (2015). Scholar
  45. 45.
    Salisbury, E., Kamar, E., Morris, M.R.: Toward scalable social alt text: conversational crowdsourcing as a tool for refining vision-to-language technology for the blind. In: Proceedings of HCOMP 2017 (2017)Google Scholar
  46. 46.
    Salisbury, E., Kamar, E., Morris, M.R.: Evaluating and complementing vision-to-language technology for people who are blind with conversational crowdsourcing. In: IJCAI, pp. 5349–5353 (2018)Google Scholar
  47. 47.
    Shuster, K., Humeau, S., Hu, H., Bordes, A., Weston, J.: Engaging image captioning via personality. arXiv preprint arXiv:1810.10665 (2018)
  48. 48.
    Srivastava, G., Srivastava, R.: A survey on automatic image captioning. In: Ghosh, D., Giri, D., Mohapatra, R.N., Savas, E., Sakurai, K., Singh, L.P. (eds.) ICMC 2018. CCIS, vol. 834, pp. 74–83. Springer, Singapore (2018). Scholar
  49. 49.
    Stangl, A., Morris, M.R., Gurari, D.: “Person, shoes, tree. Is the person naked?” What people with vision impairments want in image descriptions. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–13 (2020)Google Scholar
  50. 50.
    Vedantam, R., Lawrence Zitnick, C., Parikh, D.: CIDEr: consensus-based image description evaluation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4566–4575 (2015)Google Scholar
  51. 51.
    Von Ahn, L., Ginosar, S., Kedia, M., Liu, R., Blum, M.: Improving accessibility of the web with a computer game. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 79–82. ACM (2006)Google Scholar
  52. 52.
    Voykinska, V., Azenkot, S., Wu, S., Leshed, G.: How blind people interact with visual content on social networking services. In: Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work & Social Computing, pp. 1584–1595. ACM (2016)Google Scholar
  53. 53.
    Wu, S., Wieland, J., Farivar, O., Schiller, J.: Automatic alt-text: computer-generated image descriptions for blind users on a social network service. In: CSCW, pp. 1180–1192 (2017)Google Scholar
  54. 54.
    Xiao, J., Hays, J., Ehinger, K.A., Oliva, A., Torralba, A.: SUN database: large-scale scene recognition from abbey to zoo. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3485–3492. IEEE (2010)Google Scholar
  55. 55.
    Yang, X., Tang, K., Zhang, H., Cai, J.: Auto-encoding scene graphs for image captioning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 10685–10694 (2019)Google Scholar
  56. 56.
    Yoshikawa, Y., Shigeto, Y., Takeuchi, A.: Stair captions: constructing a large-scale Japanese image caption dataset. arXiv preprint arXiv:1705.00823 (2017)
  57. 57.
    Young, P., Lai, A., Hodosh, M., Hockenmaier, J.: From image descriptions to visual denotations: new similarity metrics for semantic inference over event descriptions. Trans. Assoc. Comput. Linguist. 2, 67–78 (2014)CrossRefGoogle Scholar
  58. 58.
    Zhong, Y., Lasecki, W.S., Brady, E., Bigham, J.P.: RegionSpeak: quick comprehensive spatial descriptions of complex images for blind users. In: Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, pp. 2353–2362. ACM (2015)Google Scholar
  59. 59.
    Zhou, B., Lapedriza, A., Xiao, J., Torralba, A., Oliva, A.: Learning deep features for scene recognition using places database. In: Advances in Neural Information Processing Systems, pp. 487–495 (2014)Google Scholar
  60. 60.
    Zitnick, C.L., Parikh, D., Vanderwende, L.: Learning the visual interpretation of sentences. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1681–1688 (2013)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.University of Texas at AustinAustinUSA

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