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arXiv · 2409.14215

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

Xin Jiang·Junwei Zheng·Ruiping Liu·Jiahang Li·Jiaming Zhang·Sven Matthiesen·Rainer Stiefelhagen
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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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Xin Jiang, Junwei Zheng, Ruiping Liu, Jiahang Li, Jiaming Zhang, Sven Matthiesen, Rainer Stiefelhagen. 2024-09-21. @Bench: Benchmarking Vision-Language Models for Human-centered Assistive Technology. https://arxiv.org/abs/2409.14215

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