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.
@article{arxiv.2001.03194,
title = {MatrixNets: A New Scale and Aspect Ratio Aware Architecture for Object Detection},
author = {Abdullah Rashwan and Rishav Agarwal and Agastya Kalra and Pascal Poupart},
journal= {arXiv preprint arXiv:2001.03194},
year = {2020}
}
Comments
This is the full paper for arXiv:1908.04646 with more applications, experiments, and ablation study