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

Same Features, Different Day: Weakly Supervised Feature Learning for Seasonal Invariance