Descriptor Distillation: a Teacher-Student-Regularized Framework for Learning Local Descriptors

arXiv:2209.11795v2 Announce Type: replace
Abstract: Learning a fast and discriminative patch descriptor is a challenging topic in computer vision. Recently, many existing works focus on training various descriptor learning networks by minimizing a triplet loss (or its variants), which is expected to decrease the distance between each positive pair and increase the distance between each negative pair. However, such an expectation has to be lowered due to the non-perfect convergence of network optimizer to a local solution. Addressing this problem …

abstract arxiv computer computer vision cs.cv distillation focus framework loss networks positive training type variants vision