MINGLE：用于城市场景中语义复杂区域检测的视觉语言模型
MINGLE: VLMs for Semantically Complex Region Detection in Urban Scenes
AI总结：
本文提出MINGLE模型，通过整合人类检测、视觉语言推理和轻量级空间聚合算法，实现城市场景中社交群体区域的检测，贡献包括新数据集和语义丰富的标注方法。
摘要：
理解公共空间中的群体层面社交互动对城市规划至关重要，需从图像中解读如关系、接近度和共移动等微妙视觉线索，这些线索超越传统目标检测。为此，我们引入社交群体区域检测任务，要求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MINGLE（Modeling INterpersonal Group-Level Engagement），一个模块化的三阶段流程，整合：（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以分类成对的社会关联，（3）轻量级的空间聚合算法以定位社交连接的群体。为支持此任务并鼓励未来研究，我们呈现包含10万张城市街景图像的新数据集，标注有边界框和个体及社交互动群体的标签。标注结合人工创建的标签和MINGLE流程的输出，确保语义丰富性和现实场景的广泛覆盖。
英文摘要：
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.
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。
