德克萨斯大学提出：One-stage目标检测最强算法 ExtremeNet

前戏

最近出了很多论文，各种SOTA。比如（点击可访问）：

商汤等提出：统一多目标跟踪框架

亚马逊提出：用于人群计数的尺度感知注意力网络

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.

Illustration of our object detection method

Illustration of our framework

Illustration of our object detection method

基础工作

Extreme and center points

Keypoint detection

CornerNet

Deep Extreme Cut

创新点

Center Grouping

Ghost box suppression

Edge aggregation

Extreme Instance Segmentation

实验结果

ExtremeNet有多强，看下面的图示就知道了，在COCO test-dev数据集上，mAP为43.2，在one-stage detector中，排名第一。可惜的是没有给出时间上的对比，论文中只介绍说测试一幅图像，耗时322ms（3.1 FPS）。

State-of-the-art comparison on COCO test-dev

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