标题： MINGLE：用于城市场景中语义复杂区域检测的视觉语言模型 显示英文标题

标题： MINGLE: VLMs for Semantically Complex Region Detection in Urban Scenes

摘要： 理解公共空间中的群体级社会互动对于城市规划至关重要，这有助于设计出社会活力和包容性环境。 从图像中检测这种互动涉及解释诸如关系、接近度和共同运动等细微的视觉线索——这些语义复杂的信号超出了传统目标检测的范围。 为了解决这个挑战，我们引入了一个社交群体区域检测任务，该任务需要推断并空间定位由抽象人际关系定义的视觉区域。 我们提出了MINGLE（建模人际群体级参与），一个模块化的三阶段流程，包括：(1) 现成的人类检测和深度估计，(2) 基于视觉语言模型的推理来分类成对的社会归属，以及(3) 一种轻量级的空间聚合算法来定位社会连接的群体。 为了支持这项任务并鼓励未来的研究，我们提供了一个包含10万张城市街景图像的新数据集，这些图像带有个人和社会互动群体的边界框和标签。 这些标注结合了人工创建的标签和MINGLE流程的输出，确保了语义丰富性和现实场景的广泛覆盖。 显示英文摘要

摘要： Understanding group-level social interactions in public spaces is crucial for urban planning, informing the design of socially vibrant and inclusive environments. Detecting such interactions from images involves interpreting subtle visual cues such as relations, proximity, and co-movement - semantically complex signals that go beyond traditional object detection. To address this challenge, we introduce a social group region detection task, which requires inferring and spatially grounding visual regions defined by abstract interpersonal relations. We propose MINGLE (Modeling INterpersonal Group-Level Engagement), a modular three-stage pipeline that integrates: (1) off-the-shelf human detection and depth estimation, (2) VLM-based reasoning to classify pairwise social affiliation, and (3) a lightweight spatial aggregation algorithm to localize socially connected groups. To support this task and encourage future research, we present a new dataset of 100K urban street-view images annotated with bounding boxes and labels for both individuals and socially interacting groups. The annotations combine human-created labels and outputs from the MINGLE pipeline, ensuring semantic richness and broad coverage of real-world scenarios.