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Quad-networks: unsupervised learning to rank for interest point detection
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This paper is the first to propose such a formulation: training a neural network to rank points in a transformation-invariant manner, and shows that this unsupervised method performs better or on-par with baselines on two tasks.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.read more
Citations
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Proceedings Article
D2-Net: A Trainable CNN for Joint Detection and Description of Local Features.
Mihai Dusmanu,Ignacio Rocco,Tomas Pajdla,Marc Pollefeys,Josef Sivic,Akihiko Torii,Torsten Sattler +6 more
TL;DR: In this paper, a single CNN is simultaneously a dense feature descriptor and a feature detector, and the obtained keypoints are more stable than their traditional counterparts based on early detection of low-level structures.
References
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Microsoft COCO: Common Objects in Context
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TL;DR: A new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of scene understanding by gathering images of complex everyday scenes containing common objects in their natural context.
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