  Focal Loss has reached incredible popularity as it uses a simple technique to
identify and utilize hard examples to achieve better performance on
classification. However, this method does not easily generalize outside of
classification tasks, such as in keypoint detection. In this paper, we propose
a novel adaptation of Focal Loss for keypoint detection tasks, called
Adversarial Focal Loss (AFL). AFL not only is semantically analogous to Focal
loss, but also works as a plug-and-chug upgrade for arbitrary loss functions.
While Focal Loss requires output from a classifier, AFL leverages a separate
adversarial network to produce a difficulty score for each input. This
difficulty score can then be used to dynamically prioritize learning on hard
examples, even in absence of a classifier. In this work, we show AFL's
effectiveness in enhancing existing methods in keypoint detection and verify
its capability to re-weigh examples based on difficulty.
