@Bench: Benchmarking Vision-Language Models for Human-centered Assistive Technology
<|reference_start|>@Bench: Benchmarking Vision-Language Models for Human-centered Assistive Technology: 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.<|reference_end|>
@article{jiang2024@bench:,
    title={@Bench: Benchmarking Vision-Language Models for Human-centered Assistive
  Technology},
    author={Xin Jiang, Junwei Zheng, Ruiping Liu, Jiahang Li, Jiaming Zhang, Sven
  Matthiesen, and Rainer Stiefelhagen},
    journal={arXiv preprint arXiv:2409.14215},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.14215},
    primaryClass={cs.CV}
}