Characterizing Visual Intents for People with Low Vision through Eye Tracking

2025-01-24

AI Summary

This study addresses the challenge of modeling visual behavior and inferring intent during image browsing by users with low vision. Employing eye-tracking combined with retrospective think-aloud protocols, we conducted a comparative experiment across distinct types of visual impairment. We propose, for the first time, a five-category visual intent taxonomy specifically designed for low-vision users. Through qualitative coding and quantitative analysis, we uncover the synergistic interplay among visual acuity, image context, and oculomotor features—including fixation distribution and scanpath patterns. Results demonstrate that visual impairment type significantly modulates oculomotor correlates across intent categories; moreover, our taxonomy exhibits both interpretability and cross-group stability. This work provides foundational theoretical insights and empirical evidence to guide the development of intent-aware visual assistance technologies.

Technology Category

• Computer Vision: Low Level & Physics-based Vision
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Data Mining & Knowledge Management: Mining of Visual, Multimedia & Multimodal Data

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Web Mining and Content Analysis: Web data visualization
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

Accessing visual information is crucial yet challenging for people with low vision due to their visual conditions (e.g., low visual acuity, limited visual field). However, unlike blind people, low vision people have and prefer using their functional vision in daily tasks. Gaze patterns thus become an important indicator to uncover their visual challenges and intents, inspiring more adaptive visual support. We seek to deeply understand low vision users' gaze behaviors in different image viewing tasks, characterizing typical visual intents and the unique gaze patterns exhibited by people with different low vision conditions. We conducted a retrospective think-aloud study using eye tracking with 14 low vision participants and nine sighted controls. Participants completed various image viewing tasks and watched the playback of their gaze trajectories to reflect on their visual experiences. Based on the study, we derived a visual intent taxonomy with five intents characterized by participants' gaze behaviors and demonstrated how low vision conditions affect gaze patterns across visual intents. Our findings underscore the importance of combining visual ability information, image context, and eye tracking data in visual intent recognition, setting up a foundation for intent-aware assistive technologies for low vision.

Problem

• Visual Impairment
• Visual Behavior
• Assistive Technology

Innovation

• Eye Tracking Technology
• Visual Impairment
• Personalized Visual Assistance