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• W2904372927 abstract "Advances in deep learning have greatly widened the scope of automatic computer vision algorithms and enable users to ask questions directly about the content in images and video. This paper explores the necessary steps towards a future Visual Data Management System (VDMS), where the predictions of such deep learning models are stored, managed, queried, and indexed. We propose a query and data model that disentangles the neural network models used, the query workload, and the data source semantics from the query processing layer. Our system, DeepLens, is based on dataflow query processing systems and this research prototype presents initial experiments to elicit important open research questions in visual analytics systems. One of our main conclusions is that any future declarative VDMS will have to revisit query optimization and automated physical design from a unified perspective of performance and accuracy tradeoffs. Physical design and query optimization choices can not only change performance by orders of magnitude, they can potentially affect the accuracy of results." @default.
• W2904372927 created "2018-12-22" @default.
• W2904372927 creator A5018480426 @default.
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• W2904372927 date "2018-12-18" @default.
• W2904372927 modified "2023-09-26" @default.
• W2904372927 title "DeepLens: Towards a Visual Data Management System" @default.
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