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.

翻译：我们以已经建立的一个名为“基于全球对比的显要区域探测”的突出图像分解技术的形式,提出了“反向自控学习增强政策”的形式。这一探测技术是为无关的计算机视野任务设计的,在实践上被观察到在SSL协议范围内发挥扩增促进者的作用。这一观察植根于SSL时尚太阳电池板的航空图像实际学习尝试,这些图像展示了具有挑战性的边界模式。在将这一技术成功地结合到我们的问题领域之后,我们制定了一个通用程序,并与受标准扩增技术制约的各种相悖的SRL算法进行了全面、系统的绩效评估。这一评估跨多个数据集进行的,表明拟议的技术确实有助于SSL。我们假设,在处理下游分解任务时,突出的图像分解是否足以作为唯一与SSL相抗的增强政策。