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.

本文提出了一种使用基于关键点估计网络的下向方法进行目标检测的算法，我们只需检测每个对象的最高、最左、最下、最右以及中心五个极点，并且如果这些点在几何上对齐，则将其分组为一个边界框，从而避免了区域分类或隐式特征学习，此算法表现出了与基于区域的检测算法相当的性能。

通过极值点和中心点分组进行自底向上的目标检测