  Object detection can be regarded as a pixel clustering task, and its boundary
is determined by four extreme points (leftmost, top, rightmost, and bottom).
However, most studies focus on the center or corner points of the object, which
are actually conditional results of the extreme points. In this paper, we
present an Extreme-Point-Prediction- Based object detector (EPP-Net), which
directly regresses the relative displacement vector between each pixel and the
four extreme points. We also propose a new metric to measure the similarity
between two groups of extreme points, namely, Extreme Intersection over Union
(EIoU), and incorporate this EIoU as a new regression loss. Moreover, we
propose a novel branch to predict the EIoU between the ground-truth and the
prediction results, and take it as the localization confidence to filter out
poor detection results. On the MS-COCO dataset, our method achieves an average
precision (AP) of 44.0% with ResNet-50 and an AP of 50.3% with ResNeXt-101-DCN.
The proposed EPP-Net provides a new method to detect objects and outperforms
state-of-the-art anchor-free detectors.
