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arXiv 2407.08882cs.HC
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面向视障人士的大多模态模型(LMM)辅助新兴实践:对设计的启示

Emerging Practices for Large Multimodal Model (LMM) Assistance for People with Visual Impairments: Implications for Design

Jingyi Xie, Rui Yu, He Zhang, Sooyeon Lee, Syed Masum Billah, John M. Carroll

AI总结:

该研究通过14名视障用户的调研,揭示了视障群体对大视觉语言模型辅助工具的有机适配方式与使用局限,为设计更目标导向、实时可靠的视障AI辅助技术提供了设计启示。

摘要:

视障人士以非视觉方式感知环境,常使用人工智能辅助工具获取视觉信息的文本描述。近期基于大视觉语言模型的人工智能工具(如Be My AI)更能理解用户的自然语言询问,并以可听文本描述场景;但目前这些工具对视障用户的有用程度仍缺乏研究。本文旨在填补这一空白。我们对14名视障用户开展的研究显示,他们正在有机地适配这些工具——这些工具不仅能促进家庭、空间和社交场景下的复杂互动,还可作为用户认知的延伸,仿佛认知分布在视觉信息之中。我们还发现,尽管当前这些工具不具备目标导向性,但用户会适应这一局限,并乐于将工具的能力用于更广泛的场景。这些发现为我们设计更具目标导向性、实时处理能力且可靠的人工智能辅助技术提供了思路。

英文摘要:

People with visual impairments perceive their environment non-visually and often use AI-powered assistive tools to obtain textual descriptions of visual information. Recent large vision-language model-based AI-powered tools like Be My AI are more capable of understanding users' inquiries in natural language and describing the scene in audible text; however, the extent to which these tools are useful to visually impaired users is currently understudied. This paper aims to fill this gap. Our study with 14 visually impaired users reveals that they are adapting these tools organically -- not only can these tools facilitate complex interactions in household, spatial, and social contexts, but they also act as an extension of users' cognition, as if the cognition were distributed in the visual information. We also found that although the tools are currently not goal-oriented, users accommodate this limitation and embrace the tools' capabilities for broader use. These findings enable us to envision design implications for creating more goal-oriented, real-time processing, and reliable AI-powered assistive technology.

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。

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