  Landmark localization is a challenging problem in computer vision with a
multitude of applications. Recent deep learning based methods have shown
improved results by regressing likelihood maps instead of regressing the
coordinates directly. However, setting the precision of these regression
targets during the training is a cumbersome process since it creates a
trade-off between trainability vs localization accuracy. Using precise targets
introduces a significant sampling bias and hence makes the training more
difficult, whereas using imprecise targets results in inaccurate landmark
detectors. In this paper, we introduce "Adaloss", an objective function that
adapts itself during the training by updating the target precision based on the
training statistics. This approach does not require setting problem-specific
parameters and shows improved stability in training and better localization
accuracy during inference. We demonstrate the effectiveness of our proposed
method in three different applications of landmark localization: 1) the
challenging task of precisely detecting catheter tips in medical X-ray images,
2) localizing surgical instruments in endoscopic images, and 3) localizing
facial features on in-the-wild images where we show state-of-the-art results on
the 300-W benchmark dataset.
