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

• URL: http://arxiv.org/abs/2409.14215v1
• Date: Sat, 21 Sep 2024 18:30:17 GMT
• Title: @Bench: Benchmarking Vision-Language Models for Human-centered Assistive Technology
• Authors: Xin Jiang, Junwei Zheng, Ruiping Liu, Jiahang Li, Jiaming Zhang, Sven Matthiesen, Rainer Stiefelhagen,
• Abstract summary: Human-centered Assistive Technologies (ATs) for helping People with Visual Impairments (PVIs) are evolving into generalists, capable of performing multiple tasks simultaneously. We first create a novel AT benchmark (@Bench) guided by a pre-design user study with PVIs. Besides, we propose a novel AT model (@Model) that addresses all tasks simultaneously and can be expanded to more assistive functions for helping PVIs.
• Score: 31.779074930032184
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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