用人类注视建模主观城市感知
Modeling Subjective Urban Perception with Human Gaze
AI总结：
本文提出基于注视行为的城市感知框架，通过结合注视数据与场景表示，提升对主观城市感知的预测能力。
摘要：
城市感知描述了人们对城市环境的主观评价，塑造了城市被体验和理解的方式。现有计算方法主要从街景图像直接建模城市感知，但忽略了人类感知过程如何形成这些判断。本文引入Place Pulse-Gaze数据集，通过同步眼动记录和个体感知标签增强街景图像。基于该数据集，我们提出Gaze-Guided Urban Perception Framework，系统研究注视行为如何影响主观城市感知的建模。框架探讨了三种互补设置：仅基于注视的建模、注视与显式语义场景表示的融合、以及注视与隐式更丰富的视觉表示的融合。实验表明，注视本身已包含有用的预测信号，结合场景表示进一步提升预测效果。整体发现强调了将人类感知过程纳入城市场景理解的重要性，并为基于注视的多模态城市计算开辟了新方向。
英文摘要：
Urban perception describes how people subjectively evaluate urban environments, shaping how cities are experienced and understood. Existing computational approaches primarily model urban perception directly from street view images, but largely ignore the human perceptual process through which such judgments are formed. In this paper, we introduce Place Pulse-Gaze, an urban perception dataset that augments street view images with synchronized eye-tracking recordings and individual perception labels. Based on this dataset, we propose a Gaze-Guided Urban Perception Framework to study how gaze behavior contributes to the modeling of subjective urban perception. The framework systematically investigates three complementary settings: gaze-only modeling, gaze fusion with explicit semantic scene representations, and gaze fusion with implicit richer visual representations. Experiments show that gaze alone already carries useful predictive signals for subjective urban perception, and that integrating gaze with scene representations further improves prediction under both semantic and richer visual representations. Overall, our findings highlight the importance of incorporating human perceptual processes into urban scene understanding and open a direction for gaze-guided multimodal urban computing.
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