  Human pose estimation has achieved significant progress on images with high
imaging resolution. However, low-resolution imagery data bring nontrivial
challenges which are still under-studied. To fill this gap, we start with
investigating existing methods and reveal that the most dominant heatmap-based
methods would suffer more severe model performance degradation from
low-resolution, and offset learning is an effective strategy. Established on
this observation, in this work we propose a novel Confidence-Aware Learning
(CAL) method which further addresses two fundamental limitations of existing
offset learning methods: inconsistent training and testing, decoupled heatmap
and offset learning. Specifically, CAL selectively weighs the learning of
heatmap and offset with respect to ground-truth and most confident prediction,
whilst capturing the statistical importance of model output in mini-batch
learning manner. Extensive experiments conducted on the COCO benchmark show
that our method outperforms significantly the state-of-the-art methods for
low-resolution human pose estimation.
