IMPACT-HOI: 開始点固定型部分HOIイベント構築のための監督制御
IMPACT-HOI: Supervisory Control for Onset-Anchored Partial HOI Event Construction
人間のデモからロボット操作を学習するための高品質な構造化データを生成するため、一人称視点の手順動画における人-物体インタラクション(HOI)のイベントグラフを、人間とAIの協調で効率的にアノテーションするフレームワークを提案した。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
4. どうやって有効だと検証した？
5. 議論はある？
6. 次に読むべき論文は？
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著者: Haoshen Zhang, Di Wen, Kunyu Peng, David Schneider, Zeyun Zhong, Alexander Jaus, Zdravko Marinov, Jiale Wei, Ruiping Liu, Junwei Zheng, Yufan Chen, Yufeng Zhang, Yuanhao Luo, Lei Qi, Rainer Stiefelhagen
分類: cs.CV, cs.AI, cs.RO
原文アブストラクト
We present IMPACT-HOI, a mixed-initiative framework for annotating egocentric procedural video by constructing structured event graphs for Human-Object Interactions (HOI), motivated by the need for high-quality structured supervision for learning robot manipulation from human demonstration. IMPACT-HOI frames this task as the incremental resolution of a partially specified, onset-anchored event state. A trust-calibrated controller selects among direct queries, human-confirmed suggestions, and conservative completions based on empirical annotator behavior and evidence quality. A risk-bounded execution protocol, utilizing atomic rollback, ensures that human-confirmed decisions are preserved against conflicting automated updates. A user study with 9 participants shows a 13.5% reduction in manual annotation actions, a 46.67% event match rate, and zero confirmed-field violations under the studied protocol. The code will be made publicly available at https://github.com/541741106/IMPACT_HOI.
