Neverova, N;
Kokkinos, I;
(2018)
Mass Displacement Networks.
In: Shao, L and Shum, HPH and Hospedales, T, (eds.)
29th British Machine Vision Conference (BMVC) 2018.
BMVA Press
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Abstract
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.
Type: | Proceedings paper |
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Title: | Mass Displacement Networks |
Event: | BMVC 2018, 29th British Machine Vision Conference, 3–6 September 2018, Newcastle upon Tyne, UK |
Open access status: | An open access version is available from UCL Discovery |
Publisher version: | http://bmvc2018.org/programmedetail.html |
Language: | English |
Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
URI: | https://discovery.ucl.ac.uk/id/eprint/10060971 |




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