  This paper presents U-LanD, a framework for joint detection of key frames and
landmarks in videos. We tackle a specifically challenging problem, where
training labels are noisy and highly sparse. U-LanD builds upon a pivotal
observation: a deep Bayesian landmark detector solely trained on key video
frames, has significantly lower predictive uncertainty on those frames vs.
other frames in videos. We use this observation as an unsupervised signal to
automatically recognize key frames on which we detect landmarks. As a test-bed
for our framework, we use ultrasound imaging videos of the heart, where sparse
and noisy clinical labels are only available for a single frame in each video.
Using data from 4,493 patients, we demonstrate that U-LanD can exceedingly
outperform the state-of-the-art non-Bayesian counterpart by a noticeable
absolute margin of 42% in R2 score, with almost no overhead imposed on the
model size. Our approach is generic and can be potentially applied to other
challenging data with noisy and sparse training labels.
