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arXiv · 1609.03532

Fully-Trainable Deep Matching

James Thewlis·Shuai Zheng·Philip H. S. Torr·Andrea Vedaldi
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Abstract

Deep Matching (DM) is a popular high-quality method for quasi-dense image matching. Despite its name, however, the original DM formulation does not yield a deep neural network that can be trained end-to-end via backpropagation. In this paper, we remove this limitation by rewriting the complete DM algorithm as a convolutional neural network. This results in a novel deep architecture for image matching that involves a number of new layer types and that, similar to recent networks for image segmentation, has a U-topology. We demonstrate the utility of the approach by improving the performance of DM by learning it end-to-end on an image matching task.

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James Thewlis, Shuai Zheng, Philip H. S. Torr, Andrea Vedaldi. 2016-09-12. Fully-Trainable Deep Matching. https://arxiv.org/abs/1609.03532

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