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.

SCE-MAE 是一个高度有效且稳健的框架，在标记数据不可用的情况下，通过使用 MAE 方法、基于特征图进行操作以及使用密度峰聚类算法和局部受限排斥损失来直接提取部分局部对应关系，大幅超越了现有 SOTA 方法约 20%-44% 的地标匹配和约 9%-15% 的地标检测任务。