发表机构
University of Rochester（罗切斯特大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对减真现实（DR）移除无关物体时易丢失有用上下文的问题，提出FocusAdapt系统，整合视觉显著性等信息预测干扰并选择性弱化干扰物体，为程序性任务提供自适应焦点辅助。
AI 中文摘要
减真现实（DR）可通过移除无关物体减少视觉杂乱，但移除所有任务无关物体可能消除有用上下文信息、降低情境意识。我们提出FocusAdapt，这是一种上下文感知的DR系统，通过整合视觉显著性、语义相关性和注视行为预测物体级干扰。基于形成性研究的发现，FocusAdapt选择性弱化高度干扰的物体，同时保留有用上下文，从而在程序性任务中实现自适应焦点辅助。
英文摘要
Diminished Reality (DR) can reduce visual clutter by removing irrelevant objects. However, removing all task-irrelevant objects may eliminate useful contextual information and reduce situational awareness. We present FocusAdapt, a context-aware DR system that predicts object-level distraction by integrating visual saliency, semantic relevance, and gaze behavior. Based on findings from a formative study, FocusAdapt selectively diminishes highly distracting objects while preserving useful context, enabling adaptive focus assistance during procedural tasks.
