Collaborative Object Detectors Adaptive to Bandwidth and Computation

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 collaborative 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.