  Convolutional Neural Networks achieve state-of-the-art accuracy in object
detection tasks. However, they have large computational and energy requirements
that challenge their deployment on resource-constrained edge devices. Object
detection takes an image as an input, and identifies the existing object
classes as well as their locations in the image. In this paper, we leverage the
prior knowledge about the probabilities that different object categories can
occur jointly to increase the efficiency of object detection models. In
particular, our technique clusters the object categories based on their spatial
co-occurrence probability. We use those clusters to design an adaptive network.
During runtime, a branch controller decides which part(s) of the network to
execute based on the spatial context of the input frame. Our experiments using
COCO dataset show that our adaptive object detection model achieves up to 45%
reduction in the energy consumption, and up to 27% reduction in the latency,
with a small loss in the average precision (AP) of object detection.
