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Deep Shape Matching

2017-09-11ECCV 2018Code Available0· sign in to hype

Filip Radenović, Giorgos Tolias, Ondřej Chum

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Abstract

We cast shape matching as metric learning with convolutional networks. We break the end-to-end process of image representation into two parts. Firstly, well established efficient methods are chosen to turn the images into edge maps. Secondly, the network is trained with edge maps of landmark images, which are automatically obtained by a structure-from-motion pipeline. The learned representation is evaluated on a range of different tasks, providing improvements on challenging cases of domain generalization, generic sketch-based image retrieval or its fine-grained counterpart. In contrast to other methods that learn a different model per task, object category, or domain, we use the same network throughout all our experiments, achieving state-of-the-art results in multiple benchmarks.

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Benchmark Results

DatasetModelMetricClaimedVerifiedStatus
ChairsEdgeMAC + whiteningR@185.6Unverified
HandbagsEdgeMAC + whiteningR@151.2Unverified
ShoesEdgeMAC + whiteningR@154.8Unverified

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