Repeatability Is Not Enough: Learning Affine Regions via Discriminability

Code

• github.com/ducha-aiki/affnet
  OfficialIn paperpytorch★ 0
• github.com/ducha-aiki/imc2021-sample-kornia-submission
  pytorch★ 21
• github.com/kornia/kornia/blob/master/kornia/feature/affine_shape.py
  pytorch★ 0

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.The source codes and trained weights are available at https://github.com/ducha-aiki/affnet

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Reproductions