  Recently, dense contrastive learning has shown superior performance on dense
prediction tasks compared to instance-level contrastive learning. Despite its
supremacy, the properties of dense contrastive representations have not yet
been carefully studied. Therefore, we analyze the theoretical ideas of dense
contrastive learning using a standard CNN and straightforward feature matching
scheme rather than propose a new complex method. Inspired by the analysis of
the properties of instance-level contrastive representations through the lens
of alignment and uniformity on the hypersphere, we employ and extend the same
lens for the dense contrastive representations to analyze their underexplored
properties. We discover the core principle in constructing a positive pair of
dense features and empirically proved its validity. Also, we introduces a new
scalar metric that summarizes the correlation between alignment-and-uniformity
and downstream performance. Using this metric, we study various facets of
densely learned contrastive representations such as how the correlation changes
over single- and multi-object datasets or linear evaluation and dense
prediction tasks. The source code is publicly available at:
https://github.com/SuperSupermoon/DenseCL-analysis
