OmniQuery: Contextually Augmenting Captured Multimodal Memory to Enable Personal Question Answering
<|reference_start|>OmniQuery: Contextually Augmenting Captured Multimodal Memory to Enable Personal Question Answering: People often capture memories through photos, screenshots, and videos. While existing AI-based tools enable querying this data using natural language, they mostly only support retrieving individual pieces of information like certain objects in photos and struggle with answering more complex queries that involve interpreting interconnected memories like event sequences. We conducted a one-month diary study to collect realistic user queries and generated a taxonomy of necessary contextual information for integrating with captured memories. We then introduce OmniQuery, a novel system that is able to answer complex personal memory-related questions that require extracting and inferring contextual information. OmniQuery augments single captured memories through integrating scattered contextual information from multiple interconnected memories, retrieves relevant memories, and uses a large language model (LLM) to comprehensive answers. In human evaluations, we show the effectiveness of OmniQuery with an accuracy of 71.5%, and it outperformed a conventional RAG system, winning or tying in 74.5% of the time.<|reference_end|>
@article{li2024omniquery:,
    title={OmniQuery: Contextually Augmenting Captured Multimodal Memory to Enable
  Personal Question Answering},
    author={Jiahao Nick Li, Zhuohao Jerry Zhang, Jiaju Ma},
    journal={arXiv preprint arXiv:2409.08250},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.08250},
    primaryClass={cs.HC cs.AI}
}