InViG：基于50万人机交互的交互式视觉定位基准测试
InViG: Benchmarking Interactive Visual Grounding with 500K Human-Robot Interactions
AI总结：
针对人机交互中语言模糊性问题，本文提出了首个大规模数据集InViG，包含超52万图像及消歧对话，并提出端到端基线方案，实现45.6%成功率，为模糊性感知的交互式视觉定位提供了实用基准。
摘要：
模糊性在人类交流中普遍存在。以往的人机交互（HRI）方法常依赖预定义的交互模板，导致在真实且开放场景中性能下降。为解决这些问题，我们提出了一个大规模数据集InViG，用于语言模糊性下的交互式视觉定位。该数据集包含超过52万张图像，并附带开放式的、以目标为导向的消歧对话，涵盖数百万个物体实例及相应的问答对。基于InViG数据集，我们进行了广泛研究，并提出了一组端到端交互式视觉消歧与定位的基线解决方案，在验证期间达到45.6%的成功率。据我们所知，InViG数据集是首个用于解决开放式交互式视觉定位的大规模数据集，为模糊性感知的HRI提供了一个实用且极具挑战性的基准。代码和数据集可在 https://openivg.github.io 获取。
英文摘要：
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}.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
