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- W3204833346 abstract "State-of-the-art camera-based deep learning methods for inventory monitoring tend to fail to generalize across different domains due to the high variance of scene settings. Large amounts of human labor are required to label and parameterize the models, making a real-world deployment impractical. In a third-party warehouse setting, supervised learning approaches are either too costly and/or inaccurate to deploy due to the need for human labor to address the diverse set of environmental factors (i.e, lighting conditions, product motion, deployment limitations)." @default.
- W3204833346 created "2021-10-11" @default.
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- W3204833346 date "2021-09-21" @default.
- W3204833346 modified "2023-10-12" @default.
- W3204833346 title "PIWIMS: Physics Informed Warehouse Inventory Monitory via Synthetic Data Generation" @default.
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- W3204833346 doi "https://doi.org/10.1145/3460418.3480415" @default.
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