Mass Displacement Networks

British Machine Vision Convention (BMVC)


Despite the large improvements in performance attained by deep learning in computer vision, one can often further improve results with some additional post-processing that exploits the geometric nature of the underlying task. This commonly involves displacing the posterior distribution of a CNN in a way that makes it more appropriate for the task at hand, e.g. better aligned with local image features, or more compact. In this work we integrate this geometric post-processing within a deep architecture, introducing a differentiable and probabilistically sound counterpart to the common geometric voting technique used for evidence accumulation in vision. We refer to the resulting neural models as Mass Displacement Networks (MDNs), and apply them to human pose estimation in two distinct setups: (a) landmark localization, where we collapse a distribution to a point, allowing for precise localization of body keypoints and (b) communication across body parts, where we transfer evidence from one part to the other, allowing for a globally consistent pose estimate. We evaluate on large-scale pose estimation benchmarks, such as MPII Human Pose and COCO datasets, and report systematic improvements.

Related Publications

All Publications

SIGGRAPH - August 9, 2021

Deep Relightable Appearance Models for Animatable Faces

Sai Bi, Stephen Lombardi, Shunsuke Saito, Tomas Simon, Shih-En Wei, Kevyn Mcphail, Ravi Ramamoorthi, Yaser Sheikh, Jason Saragih

AISTATS - April 13, 2021

Continual Learning using a Bayesian Nonparametric Dictionary of Weight Factors

Nikhil Mehta, Kevin J Liang, Vinay K Verma, Lawrence Carin

NeurIPS - December 6, 2020

Improved Sample Complexity for Incremental Autonomous Exploration in MDPs

Jean Tarbouriech, Matteo Pirotta, Michal Valko, Alessandro Lazaric

NeurIPS - December 7, 2020

Labelling unlabelled videos from scratch with multi-modal self-supervision

Yuki M. Asano, Mandela Patrick, Christian Rupprecht, Andrea Vedaldi

To help personalize content, tailor and measure ads, and provide a safer experience, we use cookies. By clicking or navigating the site, you agree to allow our collection of information on and off Facebook through cookies. Learn more, including about available controls: Cookies Policy