Demonstration of MaskSearch: Efficiently Querying Image Masks for Machine Learning Workflows
<|reference_start|>Demonstration of MaskSearch: Efficiently Querying Image Masks for Machine Learning Workflows: We demonstrate MaskSearch, a system designed to accelerate queries over databases of image masks generated by machine learning models. MaskSearch formalizes and accelerates a new category of queries for retrieving images and their corresponding masks based on mask properties, which support various applications, from identifying spurious correlations learned by models to exploring discrepancies between model saliency and human attention. This demonstration makes the following contributions:(1) the introduction of MaskSearch's graphical user interface (GUI), which enables interactive exploration of image databases through mask properties, (2) hands-on opportunities for users to explore MaskSearch's capabilities and constraints within machine learning workflows, and (3) an opportunity for conference attendees to understand how MaskSearch accelerates queries over image masks.<|reference_end|>
@article{wei2024demonstration,
    title={Demonstration of MaskSearch: Efficiently Querying Image Masks for
  Machine Learning Workflows},
    author={Lindsey Linxi Wei, Chung Yik Edward Yeung, Hongjian Yu, Jingchuan
  Zhou, Dong He, Magdalena Balazinska},
    journal={arXiv preprint arXiv:2404.06563},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.06563},
    primaryClass={cs.DB cs.LG cs.MM}
}