Learning to Localize Little Landmarks

http://vision.cs.uiuc.edu/projects/litland/

Learning to Localize, Little Landmarks

People

An algorithm to automatically learn to detect landmarks in a image specific learned order.

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.

Paper

Paper: CVPR 2016 Pdf (3.6 MB)

Citation

Saurabh Singh, Derek Hoiem and David Forsyth. Learning to Localize Little Landmarks . In Computer Vision and Pattern Recognition (2016).

BibTeX

@inproceedings{Singh2016litland,
  author = {Saurabh Singh and Derek Hoiem and David Forsyth},
  title = {Learning to Localize Little Landmarks},
  booktitle={Computer Vision and Pattern Recognition},
  year = {2016},
  url = {http://vision.cs.illinois.edu/projects/litland/}
}

Code

Coming soon.

Data

Car Door Handle Dataset. (Please see the README in the zip file for details)
Light Switch Dataset. (Please see the README in the zip file for details)