Unsupervised Learning of Object Landmarks by Factorized Spatial Embeddings

James Thewlis, Hakan Bilen, Andrea Vedaldi

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Learning automatically the structure of object categories remains an important open problem in computer vision. In this paper, we propose a novel unsupervised approach that can discover and learn landmarks in object categories, thus characterizing their structure. Our approach is based on factorizing image deformations, as induced by a viewpoint change or an object deformation, by learning a deep neural network that detects landmarks consistently with such visual effects. Furthermore, we show that the learned landmarks establish meaningful correspondences between different object instances in a category without having to impose this requirement explicitly. We assess the method qualitatively on a variety of object types, natural and man-made. We also show that our unsupervised landmarks are highly predictive of manually-annotated landmarks in face benchmark datasets, and can be used to regress these with a high degree of accuracy.
Original languageEnglish
Title of host publication2017 IEEE International Conference on Computer Vision (ICCV)
Place of PublicationVenice, Italy
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages10
ISBN (Electronic)978-1-5386-1032-9
ISBN (Print)978-1-5386-1033-6
Publication statusPublished - 25 Dec 2017
Event2017 IEEE International Conference on Computer Vision - Venice, Italy
Duration: 22 Oct 201729 Oct 2017

Publication series

ISSN (Electronic)2380-7504


Conference2017 IEEE International Conference on Computer Vision
Abbreviated titleICCV 2017
Internet address

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