zhehuderek/VIVA_Benchmark_EMNLP24

Citation text from the card

@inproceedings{hu-etal-2024-viva,
    title = "{VIVA}: A Benchmark for Vision-Grounded Decision-Making with Human Values",
    author = "Hu, Zhe  and
      Ren, Yixiao  and
      Li, Jing  and
      Yin, Yu",
    editor = "Al-Onaizan, Yaser  and
      Bansal, Mohit  and
      Chen, Yun-Nung",
    booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2024",
    address = "Miami, Florida, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.emnlp-main.137",
    pages = "2294--2311",
    abstract = "This paper introduces VIVA, a benchmark for VIsion-grounded decision-making driven by human VA. While most large vision-language models (VLMs) focus on physical-level skills, our work is the first to examine their multimodal capabilities in leveraging human values to make decisions under a vision-depicted situation. VIVA contains 1,062 images depicting diverse real-world situations and the manually annotated decisions grounded in them. Given an image there, the model should select the most appropriate action to address the situation and provide the relevant human values and reason underlying the decision. Extensive experiments based on VIVA show the limitation of VLMs in using human values to make multimodal decisions. Further analyses indicate the potential benefits of exploiting action consequences and predicted human values.",
}

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