@inproceedings{cao-etal-2022-whats,
    title = "What`s Different between Visual Question Answering for Machine {\textquotedblleft}Understanding{\textquotedblright} Versus for Accessibility?",
    author = "Cao, Yang Trista  and
      Seelman, Kyle  and
      Lee, Kyungjun  and
      Daum{\'e} III, Hal",
    editor = "He, Yulan  and
      Ji, Heng  and
      Li, Sujian  and
      Liu, Yang  and
      Chang, Chua-Hui",
    booktitle = "Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2022",
    address = "Online only",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2022.aacl-main.75/",
    doi = "10.18653/v1/2022.aacl-main.75",
    pages = "1025--1034",
    abstract = "In visual question answering (VQA), a machine must answer a question given an associated image. Recently, accessibility researchers have explored whether VQA can be deployed in a real-world setting where users with visual impairments learn about their environment by capturing their visual surroundings and asking questions. However, most of the existing benchmarking datasets for VQA focus on machine {\textquotedblleft}understanding{\textquotedblright} and it remains unclear how progress on those datasets corresponds to improvements in this real-world use case. We aim to answer this question by evaluating discrepancies between machine {\textquotedblleft}understanding{\textquotedblright} datasets (VQA-v2) and accessibility datasets (VizWiz) by evaluating a variety of VQA models. Based on our findings, we discuss opportunities and challenges in VQA for accessibility and suggest directions for future work."
}