  Contrastive learning (CL) is a form of self-supervised learning and has been
widely used for various tasks. Different from widely studied instance-level
contrastive learning, pixel-wise contrastive learning mainly helps with
pixel-wise tasks such as medical landmark detection. The counterpart to an
instance in instance-level CL is a pixel, along with its neighboring context,
in pixel-wise CL. Aiming to build better feature representation, there is a
vast literature about designing instance augmentation strategies for
instance-level CL; but there is little similar work on pixel augmentation for
pixel-wise CL with a pixel granularity. In this paper, we attempt to bridge
this gap. We first classify a pixel into three categories, namely low-,
medium-, and high-informative, based on the information quantity the pixel
contains. Inspired by the ``InfoMin" principle, we then design separate
augmentation strategies for each category in terms of augmentation intensity
and sampling ratio. Extensive experiments validate that our information-guided
pixel augmentation strategy succeeds in encoding more discriminative
representations and surpassing other competitive approaches in unsupervised
local feature matching. Furthermore, our pretrained model improves the
performance of both one-shot and fully supervised models. To the best of our
knowledge, we are the first to propose a pixel augmentation method with a pixel
granularity for enhancing unsupervised pixel-wise contrastive learning.
