Semi-supervised Pose Estimation with Geometric Latent Representations

Luis A. Perez Rey, Dmitri Jarnikov, Mike Holenderski

TL;DR: Semi-supervised method for identifying planar rotations for limited amount of labelled data.

Abstract: Pose estimation is the task of finding the orientation of an object within an image with respect to a fixed frame of reference. Current classification and regression approaches to the task require large quantities of labelled data for their purposes. The amount of labelled data for pose estimation is relatively limited. With this in mind, we propose the use of Conditional Variational Autoencoders (CVAEs) \cite{Kingma2014a} with circular latent representations to estimate the corresponding 2D rotations of an object. The method is capable of training with datasets that have an arbitrary amount of labelled images providing relatively similar performance for cases in which 10-20% of the labels for images is missing.

Keywords: Semi-supervised learning, pose estimation, angle estimation, variational autoencoders