Quad-Networks: Unsupervised Learning to Rank for Interest Point Detection

Nikolay Savinov, Akihito Seki, Lubor Ladicky, Torsten Sattler, Marc Pollefeys; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 1822-1830

Abstract


Several machine learning tasks require to represent the data using only a sparse set of interest points. An ideal detector is able to find the corresponding interest points even if the data undergo a transformation typical for a given domain. Since the task is of high practical interest in computer vision, many hand-crafted solutions were proposed. In this paper, we ask a fundamental question: can we learn such detectors from scratch? Since it is often unclear what points are "interesting", human labelling cannot be used to find a truly unbiased solution. Therefore, the task requires an unsupervised formulation. We are the first to propose such a formulation: training a neural network to rank points in a transformation-invariant manner. Interest points are then extracted from the top/bottom quantiles of this ranking. We validate our approach on two tasks: standard RGB image interest point detection and challenging cross-modal interest point detection between RGB and depth images. We quantitatively show that our unsupervised method performs better or on-par with baselines.

Related Material


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[bibtex]
@InProceedings{Savinov_2017_CVPR,
author = {Savinov, Nikolay and Seki, Akihito and Ladicky, Lubor and Sattler, Torsten and Pollefeys, Marc},
title = {Quad-Networks: Unsupervised Learning to Rank for Interest Point Detection},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {July},
year = {2017}
}