IMPACT-Scribe: 境界スケッチとクエリ計画による対話型時間行動セグメンテーション
IMPACT-Scribe: Interactive Temporal Action Segmentation with Boundary Scribbles and Query Planning
手動修正を活用して将来の協調を改善する、手続き動画の高密度ラベリングのための対話型フレームワークを提案。
著者: 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
分類: cs.CV, cs.AI
原文アブストラクト
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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