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

• URL: http://arxiv.org/abs/2305.11927v2
• Date: Fri, 15 Mar 2024 18:23:16 GMT
• Title: Evaluating how interactive visualizations can assist in finding samples where and how computer vision models make mistakes
• Authors: Hayeong Song, Gonzalo Ramos, Peter Bodik,
• Abstract summary: We present two interactive visualizations in the context of Sprite, a system for creating Computer Vision (CV) models. We study how these visualizations help Sprite's users identify (evaluate) and select (plan) images where a model is struggling and can lead to improved performance.
• Score: 1.76602679361245
• License: http://creativecommons.org/licenses/by/4.0/
• 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 in the context of Sprite, a system for creating CV classification and detection models for images originating from videos. We study how these visualizations help Sprite's users identify (evaluate) and select (plan) images where a model is struggling and can lead to improved performance, compared to a baseline condition where users used a query language. We found that users who had used the visualizations found more images across a wider set of potential types of model errors.

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