Evaluating how interactive visualizations can assist in finding samples where and how computer vision models make mistakes

arXiv:2305.11927v2 Announce Type: replace-cross
Abstract: Creating Computer Vision (CV) models remains a complex practice, despite their ubiquity. Access to data, the requirement for ML expertise, and model opacity are just a few points of complexity that limit the ability of end-users to build, inspect, and improve these models. Interactive ML perspectives have helped address some of these issues by considering a teacher in the loop where planning, teaching, and evaluating tasks take place. We present and evaluate two interactive visualizations …

abstract arxiv build complexity computer computer vision cs.cv cs.hc cs.lg data expertise interactive mistakes practice samples type vision vision models