  We present Matrix Nets (xNets), a new deep architecture for object detection.
xNets map objects with different sizes and aspect ratios into layers where the
sizes and the aspect ratios of the objects within their layers are nearly
uniform. Hence, xNets provide a scale and aspect ratio aware architecture. We
leverage xNets to enhance key-points based object detection. Our architecture
achieves mAP of 47.8 on MS COCO, which is higher than any other single-shot
detector while using half the number of parameters and training 3x faster than
the next best architecture.
