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• W3158077430 abstract "In the past few years, mobile deep-learning deployment progressed by leaps and bounds, but solutions still struggle to accommodate its severe and fluctuating operational restrictions, which include bandwidth, latency, computation, and energy. In this work, we help to bridge that gap, introducing the first configurable solution for object detection that manages the triple communication-computation-accuracy trade-off with a single set of weights. Our solution shows state-of-the-art results on COCO-2017, adding only a minor penalty on the base EfficientDet-D2 architecture. Our design is robust to the choice of base architecture and compressor and should adapt well for future architectures." @default.
• W3158077430 created "2021-05-10" @default.
• W3158077430 creator A5040525043 @default.
• W3158077430 creator A5051880838 @default.
• W3158077430 creator A5054974569 @default.
• W3158077430 date "2021-05-03" @default.
• W3158077430 modified "2023-09-25" @default.
• W3158077430 title "Single-Training Collaborative Object Detectors Adaptive to Bandwidth and Computation." @default.
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• W3158077430 hasPublicationYear "2021" @default.
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