Dense RepPoints: Representing Visual Objects with Dense Point Sets
Ze Yang, Yinghao Xu, Han Xue, Zheng Zhang Raquel Urtasun, Liwei Wang, Stephen Lin, Han Hu
Abstract
We present a new object representation, called Dense Rep-Points, which utilize a large number of points to describe the multi-grainedobject representation of both box level and pixel level. Techniques are pro-posed to efficiently process these dense points, which maintains nearconstant complexity with increasing point number. The dense RepPointsis proved to represent and learn object segment well, by a novel dis-tance transform sampling method combined with a set-to-set supervision.The novel distance transform sampling method combines the strengthof contour and grid representation, which significantly outperforms thecounter-parts using contour or grid representations. On COCO, it achieves39.6 mask AP and 48.3 bbox AP."
BibTeX
@inproceedings{eccv2020_densereppointsre,
title = {Dense RepPoints: Representing Visual Objects with Dense Point Sets},
author = {Ze Yang and Yinghao Xu and Han Xue and Zheng Zhang Raquel Urtasun and Liwei Wang and Stephen Lin and Han Hu},
booktitle = {ECCV 2020},
year = {2020}
}