Blind Users Accessing Their Training Images in Teachable Object Recognizers.

ABSTRACT: Teachable object recognizers provide a solution for a very practical need for blind people - instance level object recognition. They assume one can visually inspect the photos they provide for training, a critical and inaccessible step for those who are blind. In this work, we engineer data descriptors that address this challenge. They indicate in real time whether the object in the photo is cropped or too small, a hand is included, the photos is blurred, and how much photos vary from each other. Our descriptors are built into open source testbed iOS app, called MYCam. In a remote user study in (N = 12) blind participants' homes, we show how descriptors, even when error-prone, support experimentation and have a positive impact in the quality of training set that can translate to mod

SUBMITTER: Hong J 

PROVIDER: S-EPMC10008526 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature