  State of the art human pose estimation models continue to rely on large
quantities of labelled data for robust performance. Since labelling budget is
often constrained, active learning algorithms are important in retaining the
overall performance of the model at a lower cost. Although active learning has
been well studied in literature, few techniques are reported for human pose
estimation. In this paper, we theoretically derive expected gradient length for
regression, and propose EGL++, a novel heuristic algorithm that extends
expected gradient length to tasks where discrete labels are not available. We
achieve this by computing low dimensional representations of the original
images which are then used to form a neighborhood graph. We use this graph to:
1) Obtain a set of neighbors for a given sample, with each neighbor iteratively
assumed to represent the ground truth for gradient calculation 2) Quantify the
probability of each sample being a neighbor in the above set, facilitating the
expected gradient step. Such an approach allows us to provide an approximate
solution to the otherwise intractable task of integrating over the continuous
output domain. To validate EGL++, we use the same datasets (Leeds Sports Pose,
MPII) and experimental design as suggested by previous literature, achieving
competitive results in comparison to these methods.
