Bottom-Up Object Detection by Grouping Extreme and Center Points

Xingyi Zhou, Jiacheng Zhuo, Philipp Krahenbuhl; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 850-859

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 43.7% 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.

Related Material


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[bibtex]
@InProceedings{Zhou_2019_CVPR,
author = {Zhou, Xingyi and Zhuo, Jiacheng and Krahenbuhl, Philipp},
title = {Bottom-Up Object Detection by Grouping Extreme and Center Points},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}