Matrix Nets: A New Deep Architecture for Object Detection

Abdullah Rashwan, Agastya Kalra, Pascal Poupart; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2019, pp. 0-0

Abstract


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.

Related Material


[pdf]
[bibtex]
@InProceedings{Rashwan_2019_ICCV,
author = {Rashwan, Abdullah and Kalra, Agastya and Poupart, Pascal},
title = {Matrix Nets: A New Deep Architecture for Object Detection},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops},
month = {Oct},
year = {2019}
}