Creating Computer Vision (CV) models remains a complex and taxing practice for 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. To improve the experience of end-users with various levels of ML expertise, we designed and evaluated 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, as part of the machine teaching loop, help users identify (evaluate) and select (plan) images where a model is struggling and improve the model being trained. We found that users who had used the visualizations found more images across a wider set of potential types of model errors, as well as in assessing and contrasting the prediction behavior of one or more models, thus reducing the potential effort required to improve a model.

翻译：构建计算机视觉（CV）模型对于最终用户而言仍是一项复杂且繁重的工作，涉及模型构建、检查与改进。交互式机器学习视角通过引入"教师参与回路"（教师参与规划、教学与评估任务）部分缓解了这些问题。为提升不同机器学习专业水平用户的体验，我们以Sprite系统为背景——该系统支持从视频图像创建CV分类与检测模型——设计并评估了两种交互式可视化工具。我们研究了这些可视化工具在机器学习教学回路中如何帮助用户识别（评估）与筛选（规划）模型处理困难的图像，并改进正在训练的模型。研究发现，使用可视化工具的用户能在更广泛的潜在模型错误类型中定位更多图像，同时能有效评估与对比一个或多个模型的预测行为，从而降低改进模型所需的潜在工作量。