  This paper introduces VIVA, a benchmark for VIsion-grounded decision-making
driven by human VAlues. 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.
