VHAKG: A Multi-modal Knowledge Graph Based on Synchronized Multi-view Videos of Daily Activities
<|reference_start|>VHAKG: A Multi-modal Knowledge Graph Based on Synchronized Multi-view Videos of Daily Activities: Multi-modal knowledge graphs (MMKGs), which ground various non-symbolic data (e.g., images and videos) into symbols, have attracted attention as resources enabling knowledge processing and machine learning across modalities. However, the construction of MMKGs for videos consisting of multiple events, such as daily activities, is still in the early stages. In this paper, we construct an MMKG based on synchronized multi-view simulated videos of daily activities. Besides representing the content of daily life videos as event-centric knowledge, our MMKG also includes frame-by-frame fine-grained changes, such as bounding boxes within video frames. In addition, we provide support tools for querying our MMKG. As an application example, we demonstrate that our MMKG facilitates benchmarking vision-language models by providing the necessary vision-language datasets for a tailored task.<|reference_end|>
@article{egami2024vhakg:,
    title={VHAKG: A Multi-modal Knowledge Graph Based on Synchronized Multi-view
  Videos of Daily Activities},
    author={Shusaku Egami, Takahiro Ugai, Swe Nwe Nwe Htun, Ken Fukuda},
    journal={arXiv preprint arXiv:2408.14895},
    year={2024},
    doi={10.1145/3627673.3679175},
    archivePrefix={arXiv},
    eprint={2408.14895},
    primaryClass={cs.AI cs.CL cs.CV}
}