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We present MatrixNets (xNets), a new deep architecture for object detection.xNets map objects with similar sizes and aspect ratios into many specializedlayers, allowing xNets to provide a scale and aspect ratio aware architecture.We leverage xNets to enhance single-stage object detection frameworks. First,we apply xNets on anchor-based object detection, for which we predict objectcenters and regress the top-left and bottom-right corners. Second, we useMatrixNets for corner-based object detection by predicting top-left andbottom-right corners. Each corner predicts the center location of the object.We also enhance corner-based detection by replacing the embedding layer withcenter regression. Our final architecture achieves mAP of 47.8 on MS COCO,which is higher than its CornerNet counterpart by +5.6 mAP while also closingthe gap between single-stage and two-stage detectors. The code is available athttps://github.com/arashwan/matrixnet.

MatrixNets: A New Scale and Aspect Ratio Aware Architecture for Object Detection