  We propose an augmentation policy for Contrastive Self-Supervised Learning
(SSL) in the form of an already established Salient Image Segmentation
technique entitled Global Contrast based Salient Region Detection. This
detection technique, which had been devised for unrelated Computer Vision
tasks, was empirically observed to play the role of an augmentation facilitator
within the SSL protocol. This observation is rooted in our practical attempts
to learn, by SSL-fashion, aerial imagery of solar panels, which exhibit
challenging boundary patterns. Upon the successful integration of this
technique on our problem domain, we formulated a generalized procedure and
conducted a comprehensive, systematic performance assessment with various
Contrastive SSL algorithms subject to standard augmentation techniques. This
evaluation, which was conducted across multiple datasets, indicated that the
proposed technique indeed contributes to SSL. We hypothesize whether salient
image segmentation may suffice as the only augmentation policy in Contrastive
SSL when treating downstream segmentation tasks.
