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Semi-Supervised Semantic Matching

2019-01-24
Zakaria Laskar, Juho Kannala

Abstract

Convolutional neural networks (CNNs) have been successfully applied to solve the problem of correspondence estimation between semantically related images. Due to non-availability of large training datasets, existing methods resort to self-supervised or unsupervised training paradigm. In this paper we propose a semi-supervised learning framework that imposes cyclic consistency constraint on unlabeled image pairs. Together with the supervised loss the proposed model achieves state-of-the-art on a benchmark semantic matching dataset.

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URL

http://arxiv.org/abs/1901.08339

PDF

http://arxiv.org/pdf/1901.08339


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