  Self-supervised landmark estimation is a challenging task that demands the
formation of locally distinct feature representations to identify sparse facial
landmarks in the absence of annotated data. To tackle this task, existing
state-of-the-art (SOTA) methods (1) extract coarse features from backbones that
are trained with instance-level self-supervised learning (SSL) paradigms, which
neglect the dense prediction nature of the task, (2) aggregate them into
memory-intensive hypercolumn formations, and (3) supervise lightweight
projector networks to naively establish full local correspondences among all
pairs of spatial features. In this paper, we introduce SCE-MAE, a framework
that (1) leverages the MAE, a region-level SSL method that naturally better
suits the landmark prediction task, (2) operates on the vanilla feature map
instead of on expensive hypercolumns, and (3) employs a Correspondence
Approximation and Refinement Block (CARB) that utilizes a simple density peak
clustering algorithm and our proposed Locality-Constrained Repellence Loss to
directly hone only select local correspondences. We demonstrate through
extensive experiments that SCE-MAE is highly effective and robust,
outperforming existing SOTA methods by large margins of approximately 20%-44%
on the landmark matching and approximately 9%-15% on the landmark detection
tasks.
