We developed a novel SSL approach to capture global consistency and
pixel-level local consistencies between differently augmented views of the same
images to accommodate downstream discriminative and dense predictive tasks. We
adopted the teacher-student architecture used in previous contrastive SSL
methods. In our method, the global consistency is enforced by aggregating the
compressed representations of augmented views of the same image. The
pixel-level consistency is enforced by pursuing similar representations for the
same pixel in differently augmented views. Importantly, we introduced an
uncertainty-aware context stabilizer to adaptively preserve the context gap
created by the two views from different augmentations. Moreover, we used Monte
Carlo dropout in the stabilizer to measure uncertainty and adaptively balance
the discrepancy between the representations of the same pixels in different
views.

本文提出了一种基于 SSL（自监督学习）的方法，通过全局一致性和像素级局部一致性来捕捉不同增强视图之间的一致性，引入了基于不确定性的上下文稳定器，使用 MC dropout 来衡量不确定性，并自适应平衡不同视图中相同像素的表征之间的差异。