Learning to localize little landmarks

Saurabh Singh, Derek Hoiem, David Forsyth

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

Abstract

We interact everyday with tiny objects such as the door handle of a car or the light switch in a room. These little landmarks are barely visible and hard to localize in images. We describe a method to find such landmarks by finding a sequence of latent landmarks, each with a prediction model. Each latent landmark predicts the next in sequence, and the last localizes the target landmark. For example, to find the door handle of a car, our method learns to start with a latent landmark near the wheel, as it is globally distinctive, subsequent latent landmarks use the context from the earlier ones to get closer to the target. Our method is supervised solely by the location of the little landmark and displays strong performance on more difficult variants of established tasks and on two new tasks.

Original languageEnglish (US)
Title of host publicationProceedings - 29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016
PublisherIEEE Computer Society
Pages260-269
Number of pages10
ISBN (Electronic)9781467388504
DOIs
StatePublished - Dec 9 2016
Event29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016 - Las Vegas, United States
Duration: Jun 26 2016Jul 1 2016

Publication series

NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Volume2016-December
ISSN (Print)1063-6919

Conference

Conference29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016
CountryUnited States
CityLas Vegas
Period6/26/167/1/16

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

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  • Cite this

    Singh, S., Hoiem, D., & Forsyth, D. (2016). Learning to localize little landmarks. In Proceedings - 29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016 (pp. 260-269). [7780404] (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition; Vol. 2016-December). IEEE Computer Society. https://doi.org/10.1109/CVPR.2016.35