October 22, 2017
Focal Loss for Dense Object Detection
International Conference on Computer Vision (ICCV)
In this paper, we investigate why one-stage detectors that are applied over a regular, dense sampling of possible object locations have the potential to be faster and simpler, but have trailed the accuracy of two-stage detectors thus far. We design and train a simple dense detector we call RetinaNet. Our results show that when trained with the focal loss, RetinaNet is able to match the speed of previous one-stage detectors while surpassing the accuracy of all existing state-of-the-art two-stage detectors.
By: Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, Piotr Dollar