<> <http://www.w3.org/2000/01/rdf-schema#comment> "The repository administrator has not yet configured an RDF license."^^<http://www.w3.org/2001/XMLSchema#string> .
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<https://discovery.ucl.ac.uk/id/eprint/10060971> <http://purl.org/dc/terms/title> "Mass Displacement Networks"^^<http://www.w3.org/2001/XMLSchema#string> .
<https://discovery.ucl.ac.uk/id/eprint/10060971> <http://purl.org/ontology/bibo/abstract> "Despite the large improvements in performance attained by deep learning in computer\r\nvision, one can often further improve results with some additional post-processing\r\nthat exploits the geometric nature of the underlying task. This commonly involves displacing\r\nthe posterior distribution of a CNN in a way that makes it more appropriate for\r\nthe task at hand, e.g. better aligned with local image features, or more compact. In this\r\nwork we integrate this geometric post-processing within a deep architecture, introducing\r\na differentiable and probabilistically sound counterpart to the common geometric voting\r\ntechnique used for evidence accumulation in vision. We refer to the resulting neural models\r\nas Mass Displacement Networks (MDNs), and apply them to human pose estimation\r\nin two distinct setups: (a) landmark localization, where we collapse a distribution to a\r\npoint, allowing for precise localization of body keypoints and (b) communication across\r\nbody parts, where we transfer evidence from one part to the other, allowing for a globally\r\nconsistent pose estimate. We evaluate on large-scale pose estimation benchmarks, such\r\nas MPII Human Pose and COCO datasets, and report systematic improvements."^^<http://www.w3.org/2001/XMLSchema#string> .
<https://discovery.ucl.ac.uk/id/eprint/10060971> <http://purl.org/dc/terms/date> "2018-09" .
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