Evaluating how interactive visualizations can assist in finding samples where and how computer vision models make mistakes
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arXiv
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| Format: | Preprint |
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2023
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| _version_ | 1866929278472421376 |
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| author | Song, Hayeong Ramos, Gonzalo Bodik, Peter |
| author_facet | Song, Hayeong Ramos, Gonzalo Bodik, Peter |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2305_11927 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Evaluating how interactive visualizations can assist in finding samples where and how computer vision models make mistakes Song, Hayeong Ramos, Gonzalo Bodik, Peter Human-Computer Interaction Computer Vision and Pattern Recognition Machine Learning 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. |
| title | Evaluating how interactive visualizations can assist in finding samples where and how computer vision models make mistakes |
| topic | Human-Computer Interaction Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2305.11927 |