  We address a core problem of computer vision: Detection and description of 2D
feature points for image matching. For a long time, hand-crafted designs, like
the seminal SIFT algorithm, were unsurpassed in accuracy and efficiency.
Recently, learned feature detectors emerged that implement detection and
description using neural networks. Training these networks usually resorts to
optimizing low-level matching scores, often pre-defining sets of image patches
which should or should not match, or which should or should not contain key
points. Unfortunately, increased accuracy for these low-level matching scores
does not necessarily translate to better performance in high-level vision
tasks. We propose a new training methodology which embeds the feature detector
in a complete vision pipeline, and where the learnable parameters are trained
in an end-to-end fashion. We overcome the discrete nature of key point
selection and descriptor matching using principles from reinforcement learning.
As an example, we address the task of relative pose estimation between a pair
of images. We demonstrate that the accuracy of a state-of-the-art
learning-based feature detector can be increased when trained for the task it
is supposed to solve at test time. Our training methodology poses little
restrictions on the task to learn, and works for any architecture which
predicts key point heat maps, and descriptors for key point locations.
