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Blind Users Accessing Their Training Images in Teachable Object Recognizers.

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Summary
This summary is machine-generated.

Blind individuals can now train object recognition models using the MYCam app. Data descriptors assist in creating better training sets, improving model performance despite some tedium.

Keywords:
blindmachine teachingobject recognitionparticipatory machine learningvisual impairment

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Area of Science:

  • Human-Computer Interaction
  • Computer Vision
  • Assistive Technology

Background:

  • Teachable object recognizers offer object recognition capabilities for blind individuals.
  • A key limitation is the inaccessibility of visual inspection for training data.
  • This study addresses the need for non-visual training data quality assessment.

Purpose of the Study:

  • To engineer data descriptors for real-time feedback during object recognition model training.
  • To evaluate the usability and impact of these descriptors in a user study with blind participants.
  • To explore the balance between descriptor utility, training time, and cognitive load.

Main Methods:

  • Development of real-time data descriptors indicating photo quality (cropping, blur, hand presence, variation).
  • Integration of descriptors into an open-source iOS app, MYCam.
  • Remote user study with 12 blind participants assessing descriptor utility and training experience.

Main Results:

  • Descriptors, even with errors, supported experimentation and improved training set quality.
  • Improved training sets showed potential for enhanced model performance, though gains were not uniform.
  • Participants found the app easy to use and descriptors helpful, but training was perceived as tedious.

Conclusions:

  • Engineered data descriptors can aid blind users in training object recognition models.
  • User feedback highlights the need to balance informative feedback with user time and cognitive effort.
  • Further research should focus on optimizing the training process for efficiency and reduced tedium.