“Hands On” Visual Recognition for Visually Impaired Users

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ORBIT: A Real-World Few-Shot Dataset for Teachable Object Recognition (2021, background): “Other high-variation datasets include those collected by people who are blind/lowvision [18, 41, 13] (see IncluSet for a repository of accessibility datasets [19]) however, most are not appropriate for few-shot learning.”
Toward User-Driven Sound Recognizer Personalization with People Who Are d/Deaf or Hard of Hearing (2021, background): “, [41, 49, 69]) to support blind and low-vision people in training personal object recognizers, we explore the parallel question of how DHH people can train personal sound recognizers.”
Hands Holding Clues for Object Recognition in Teachable Machines (2019, methodology): “Informed by this design space, we explore the presence of users’ hands in photos during prediction for a crowdsourced solution, such as VizWiz [11], and a teachable solution, such as Sosa-García and Odone [62]; they released their datasets (Sec.”
Teachable machines for accessibility (2017, background): “, photos of everyday objects from video streams provided by blind users [22].”