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

TL;DR
IMPACT-Scribe is a correction-driven framework that improves dense temporal action annotation by leveraging human corrections to enhance future labeling efficiency and accuracy.
Contribution
It introduces a novel interactive approach combining uncertainty-aware supervision, query planning, and correction-driven adaptation for better human-machine collaboration.
Findings
Improves labeling quality per effort in dense temporal annotation.
Enhances boundary accuracy in action segmentation.
Fosters better human-machine interaction over time.
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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