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}{this https URL}.

大规模的互动视觉消除歧义和接地数据集通过开放性目标导向的消歧对话提供了针对人机交互中的歧义性行为的实用且具有挑战性的评测基准。