EGL++: Extending Expected Gradient Length to Active Learning for Human Pose Estimation

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.