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@Bench: Benchmarking Vision-Language Models for Human-centered Assistive Technology

IEEE/CVF Winter Conference on Applications of Computer Vision(2025)

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Abstract
As Vision-Language Models (VLMs) advance, human-centered Assistive Technologies (ATs) for helping People with Visual Impairments (PVIs) are evolving into generalists, capable of performing multiple tasks simultaneously. However, benchmarking VLMs for ATs remains under-explored. To bridge this gap, we first create a novel AT benchmark (@Bench). Guided by a pre-design user study with PVIs, our benchmark includes the five most crucial vision-language tasks: Panoptic Segmentation, Depth Estimation, Optical Character Recognition (OCR), Image Captioning, and Visual Question Answering (VQA). Besides, we propose a novel AT model (@Model) that addresses all tasks simultaneously and can be expanded to more assistive functions for helping PVIs. Our framework exhibits outstanding performance across tasks by integrating multi-modal information, and it offers PVIs a more comprehensive assistance. Extensive experiments prove the effectiveness and generalizability of our framework.
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vlm,assistive technology,panoptic segmentation,depth estimation,ocr,image captioning,vqa
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要点】:本文提出了一个针对视觉障碍人士的人 centered 辅助技术的新型基准@Bench,并设计了一个可以同时执行多个任务的视觉语言模型@Model,提高了辅助技术的综合性能。

方法】:作者通过结合多模态信息,设计了一个能够同时处理五个关键视觉语言任务的模型@Model,并在模型设计中考虑了视觉障碍人士的实际需求。

实验】:作者使用了@Bench数据集,对提出的@Model模型进行了广泛的实验验证,证明了模型在各项任务中的有效性和泛化能力。