  We present MatrixNets (xNets), a new deep architecture for object detection.
xNets map objects with similar sizes and aspect ratios into many specialized
layers, 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 object
centers and regress the top-left and bottom-right corners. Second, we use
MatrixNets for corner-based object detection by predicting top-left and
bottom-right corners. Each corner predicts the center location of the object.
We also enhance corner-based detection by replacing the embedding layer with
center 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 closing
the gap between single-stage and two-stage detectors. The code is available at
https://github.com/arashwan/matrixnet.
