InViG: 50万件の人間-ロボット対話による対話型視覚的グラウンディングのベンチマーク
InViG: Benchmarking Interactive Visual Grounding with 500K Human-Robot Interactions
言語の曖昧さを含む対話を通じて対象物体を特定する対話型視覚的グラウンディングのための大規模データセットInViGを構築し、ベースライン手法を提案・評価した。
著者: Hanbo Zhang, Jie Xu, Yuchen Mo, Tao Kong
分類: cs.RO, cs.CV
原文アブストラクト
Ambiguity is ubiquitous in human communication. Previous approaches in Human-Robot Interaction (HRI) have often relied on predefined interaction templates, leading to reduced performance in realistic and open-ended scenarios. To address these issues, we present a large-scale dataset, \invig, for interactive visual grounding under language ambiguity. Our dataset comprises over 520K images accompanied by open-ended goal-oriented disambiguation dialogues, encompassing millions of object instances and corresponding question-answer pairs. Leveraging the \invig dataset, we conduct extensive studies and propose a set of baseline solutions for end-to-end interactive visual disambiguation and grounding, achieving a 45.6\% success rate during validation. To the best of our knowledge, the \invig dataset is the first large-scale dataset for resolving open-ended interactive visual grounding, presenting a practical yet highly challenging benchmark for ambiguity-aware HRI. Codes and datasets are available at: \href{https://openivg.github.io}{https://openivg.github.io}.
