[论文] EgoGroups: A Benchmark For Detecting Social Groups of People in the Wi...

论文概要

研究领域: CV 作者: Jeffri Murrugarra-Llerena, Pranav Chitale, Zicheng Liu, Kai Ao, Yujin Ham, Guha Balakrishnan, Paola Cascante-Bonilla 发布时间: 2026-03-23 arXiv: 2603.22249

中文摘要

社交群体检测，即识别参与互惠人际互动的人类（如家庭成员、朋友、顾客和商家），是智能体在现实世界中进行交易所需社交智能的关键组成部分。现有的社交群体检测基准受限于场景多样性不足和对第三人称摄像头（如监控录像）的依赖。因此，这些基准通常缺乏在多样化文化背景和无约束环境中群体如何形成和演变的真实世界评估。为解决这一空白，我们推出了EgoGroups，一个第一人称视角数据集，捕捉全球城市的社交动态。EgoGroups涵盖65个国家，覆盖低、中、高人群密度设置，以及四种天气/时段条件。我们提供了密集的人物和社交群体人工标注，以及丰富的地理和场景元数据。利用该数据集，我们对最先进的视觉语言模型/大语言模型和监督模型进行了广泛的群体检测能力评估。

原文摘要

Social group detection, or the identification of humans involved in reciprocal interpersonal interactions (e.g., family members, friends, and customers and merchants), is a crucial component of social intelligence needed for agents transacting in the world. The few existing benchmarks for social group detection are limited by low scene diversity and reliance on third-person camera sources (e.g., surveillance footage). Consequently, these benchmarks generally lack real-world evaluation on how groups形成和演变 in diverse cultural contexts and unconstrained settings. To address this gap, we introduce EgoGroups, a first-person view dataset that captures social dynamics in cities around the world. EgoGroups spans 65 countries covering low, medium, and high-crowd settings under four weather/time-of-d...

自动采集于 2026-03-25

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