Saliency Can Be All You Need In Contrastive Self-Supervised Learning
This work addresses the challenge of learning from aerial imagery with difficult boundary patterns, such as solar panels, but appears incremental as it adapts an existing technique to SSL.
The paper tackles the problem of improving contrastive self-supervised learning (SSL) by proposing a saliency-based augmentation policy using Global Contrast based Salient Region Detection, which was empirically shown to contribute to SSL performance across multiple datasets.
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.