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

arXiv: https://arxiv.org/abs/2001.03194 · HTML: https://mlsanity.com/paper/2001.03194/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jan 2020.

## Built on by (most cited first, 3 of 3)

- [SiamCorners: Siamese Corner Networks for Visual Tracking](https://mlsanity.com/paper/2104.07303/) (2021)
- [LF-YOLO: A Lighter and Faster YOLO for Weld Defect Detection of X-ray Image](https://mlsanity.com/paper/2110.15045/) (2021)
- [The Untapped Potential of Off-the-Shelf Convolutional Neural Networks](https://mlsanity.com/paper/2103.09891/) (2021)

## Abstract (arXiv)

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.