Title: Bottom-up Object Detection by Grouping Extreme and Center Points,
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.