德克萨斯大学提出：One-stage目标检测最强算法 ExtremeNet

2019 年 12 月 31 日
笔记

前戏

最近出了很多论文，各种SOTA。比如（点击可访问）：

今天头条推送的是目前人脸检测方向的SOTA论文：改进SRN人脸检测算法。本文要介绍的是目前(2019-01-26) one-stage目标检测中最强算法：ExtremeNet。

正文

《Bottom-up Object Detection by Grouping Extreme and Center Points》

arXiv: https://arxiv.org/abs/1901.08043

github: https://github.com/xingyizhou/ExtremeNet

作者团队：UT Austin

注：2019年01月23日刚出炉的paper

Abstract：With the advent of deep learning, object detection drifted from a bottom-up to a top-down recognition problem. State of the art algorithms enumerate a near-exhaustive list of object locations and classify each into: object or not. In this paper, we show that bottom-up approaches still perform competitively. We detect four extreme points (top-most, left-most, bottom-most, right-most) and one center point of objects using a standard keypoint estimation network. We group the five keypoints into a bounding box if they are geometrically aligned. Object detection is then a purely appearance-based keypoint estimation problem, without region classification or implicit feature learning. The proposed method performs on-par with the state-of-the-art region based detection methods, with a bounding box AP of 43.2% on COCO test-dev. In addition, our estimated extreme points directly span a coarse octagonal mask, with a COCO Mask AP of 18.9%, much better than the Mask AP of vanilla bounding boxes. Extreme point guided segmentation further improves this to 34.6% Mask AP.