让标注更智能：通过纠错反馈持续优化人机协作的时序动作分割
IMPACT-Scribe: Interactive Temporal Action Segmentation with Boundary Scribbles and Query Planning
- 基于边界涂鸦与查询规划，实现标注纠错驱动的闭环优化
- 相比传统方法，单位努力下标注质量提升，边界精度显著提高
- 适合需要高效高质量时序标注的研究者与标注团队
对程序性活动视频进行密集时序标注对于动作理解与具身智能至关重要，但现有反应式工具仍依赖大量人工，且每次修正被视为孤立操作，无法复用标注者不确定性与模型可靠性信息。我们提出IMPACT-Scribe，一种以修正为驱动的密集标注框架，利用每次修正来改进后续的人机协作。该框架结合了不确定性感知的边界涂鸦监督、局部提议建模、成本感知查询规划、结构化传播及修正驱动适应机制。实验与人类研究显示，这种闭环设计在单位努力下提升了标注质量，增强了边界准确性，并随时间改善人机协作效果。代码将公开于https://github.com/BanzQians/IMPACT_AS。
原文摘要 · Abstract (English)
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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