  In this paper, we explore how three related tasks, namely keypoint detection,
description, and image retrieval can be jointly tackled using a single unified
framework, which is trained without the need of training data with point to
point correspondences. By leveraging diverse information from sequential layers
of a standard ResNet-based architecture, we are able to extract keypoints and
descriptors that encode local information using generic techniques such as
local activation norms, channel grouping and dropping, and self-distillation.
Subsequently, global information for image retrieval is encoded in an
end-to-end pipeline, based on pooling of the aforementioned local responses. In
contrast to previous methods in local matching, our method does not depend on
pointwise/pixelwise correspondences, and requires no such supervision at all
i.e. no depth-maps from an SfM model nor manually created synthetic affine
transformations. We illustrate that this simple and direct paradigm, is able to
achieve very competitive results against the state-of-the-art methods in
various challenging benchmark conditions such as viewpoint changes, scale
changes, and day-night shifting localization.
