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:link: https://paperswithcode.com/paper/repeatability-is-not-enough-learning-affine
**Affnet: Repeatability Is Not Enough: Learning Affine Regions via Discriminability**
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**Abstract:** A method for learning local affine-covariant regions is presented. We show that maximizing geometric repeatability does not lead to local regions, a.k.a features,that are reliably matched and this necessitates descriptor-based learning. We explore factors that influence such learning and registration: the loss function, descriptor type, geometric parametrization and the trade-off between matchability and geometric accuracy and propose a novel hard negative-constant loss function for learning of affine regions. The affine shape estimator -- AffNet -- trained with the hard negative-constant loss outperforms the state-of-the-art in bag-of-words image retrieval and wide baseline stereo. The proposed training process does not require precisely geometrically aligned patches.
**Tasks:** Image Retrieval
**Datasets:** Oxford5k, HPatches
**Conference:** ECCV 2018
**Licence:** MIT
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**Authors:** Dmytro Mishkin, Filip Radenovic, Jiri Matas
.. image:: https://raw.githubusercontent.com/ducha-aiki/affnet/master/imgs/graf16HesAffNet.jpg
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