IMPACT-Scribe: Interactive Temporal Action Segmentation with Boundary Scribbles and Query Planning

Qian Yin; Di Wen; Kunyu Peng; David Schneider; Zeyun Zhong; Alexander Jaus; Zdravko Marinov; Jiale Wei; Ruiping Liu; Junwei Zheng; Yufan Chen; Chen Zhang; Lei Qi; Rainer Stiefelhagen

arXiv · 2605.01668

IMPACT-Scribe: Interactive Temporal Action Segmentation with Boundary Scribbles and Query Planning

Qian Yin·Di Wen·Kunyu Peng·David Schneider·Zeyun Zhong·Alexander Jaus·Zdravko Marinov·Jiale Wei·Ruiping Liu·Junwei Zheng·Yufan Chen·Chen Zhang·Lei Qi·Rainer Stiefelhagen

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Abstract

Dense temporal annotation of procedural activity videos is vital for action understanding and embodied intelligence but remains labor-intensive due to reactive tools. Each correction is treated as an isolated edit, limiting reuse of information on annotator uncertainty and model reliability. We introduce IMPACT-Scribe, a correction-driven framework for dense labeling that uses each correction to improve future human-machine collaboration. IMPACT-Scribe combines uncertainty-aware boundary scribble supervision, local proposal modeling, cost-aware query planning, structured propagation, and correction-driven adaptation. Experiments and a human study show that this closed-loop design improves labeling quality per effort, enhances boundary accuracy, and fosters better human-machine interaction over time. The code will be made publicly available at https://github.com/BanzQians/IMPACT_AS.

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Qian Yin, Di Wen, Kunyu Peng, David Schneider, Zeyun Zhong, Alexander Jaus, Zdravko Marinov, Jiale Wei, Ruiping Liu, Junwei Zheng, Yufan Chen, Chen Zhang, Lei Qi, Rainer Stiefelhagen. 2026-05-03. IMPACT-Scribe: Interactive Temporal Action Segmentation with Boundary Scribbles and Query Planning. https://arxiv.org/abs/2605.01668

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