  "Like night and day" is a commonly used expression to imply that two things
are completely different. Unfortunately, this tends to be the case for current
visual feature representations of the same scene across varying seasons or
times of day. The aim of this paper is to provide a dense feature
representation that can be used to perform localization, sparse matching or
image retrieval, regardless of the current seasonal or temporal appearance.
  Recently, there have been several proposed methodologies for deep learning
dense feature representations. These methods make use of ground truth
pixel-wise correspondences between pairs of images and focus on the spatial
properties of the features. As such, they don't address temporal or seasonal
variation. Furthermore, obtaining the required pixel-wise correspondence data
to train in cross-seasonal environments is highly complex in most scenarios.
  We propose Deja-Vu, a weakly supervised approach to learning season invariant
features that does not require pixel-wise ground truth data. The proposed
system only requires coarse labels indicating if two images correspond to the
same location or not. From these labels, the network is trained to produce
"similar" dense feature maps for corresponding locations despite environmental
changes. Code will be made available at:
https://github.com/jspenmar/DejaVu_Features
