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Snapper: Accelerating Bounding Box Annotation in Object Detection Tasks with Find-and-Snap Tooling

Authors:

Alex C Williams,

Tristan J Mckinney,

Amy Rechkemmer,

Koushik Kalyanaraman,

Patrick Haffner,

Erran LiAuthors Info & Claims

IUI '24: Proceedings of the 29th International Conference on Intelligent User Interfaces

Pages 471 - 488

https://doi.org/10.1145/3640543.3645162

Published: 05 April 2024 Publication History

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Abstract

Object detection tasks are central to the development of datasets and algorithms in computer vision and machine learning. Despite its centrality, object detection remains tedious and time-consuming due to the inherent interactions that are often associated with drawing precise annotations. In this paper, we introduce Snapper, an interactive and intelligent annotation tool that intercepts bounding box annotations as they’re drawn and “snaps” them to the nearby object edges in real-time. Through a mixed-design user study with 18 full-time annotators, we compare Snapper’s annotation mode to alternative modes of annotation and find that Snapper enables participants to complete object detection tasks 39% more quickly without diminishing annotation quality. Further, we find that participants perceive Snapper as a tool that is interactively intuitive, trustworthy, and helpful. We conclude by discussing the implications of our findings as they relate to augmenting annotators’ conventions for drawing annotations in practice.

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Cited By

Rossetto LSarasua CBernstein ACai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Estimating the Semantic Density of Visual MediaProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681594(4601-4609)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681594

Index Terms

Snapper: Accelerating Bounding Box Annotation in Object Detection Tasks with Find-and-Snap Tooling
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Object detection
2. Human-centered computing
  1. Human computer interaction (HCI)

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IUI '24: Proceedings of the 29th International Conference on Intelligent User Interfaces

March 2024

955 pages

ISBN:9798400705083

DOI:10.1145/3640543

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March 18 - 21, 2024

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Cited By

Rossetto LSarasua CBernstein ACai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Estimating the Semantic Density of Visual MediaProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681594(4601-4609)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681594

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