We study a challenging problem of unsupervised discovery of object landmarks.
Many recent methods rely on bottlenecks to generate 2D Gaussian heatmaps
however, these are limited in generating informed heatmaps while training,
presumably due to the lack of effective structural cues. Also, it is assumed
that all predicted landmarks are semantically relevant despite having no ground
truth supervision. In the current work, we introduce a consistency-guided
bottleneck in an image reconstruction-based pipeline that leverages landmark
consistency, a measure of compatibility score with the pseudo-ground truth to
generate adaptive heatmaps. We propose obtaining pseudo-supervision via forming
landmark correspondence across images. The consistency then modulates the
uncertainty of the discovered landmarks in the generation of adaptive heatmaps
which rank consistent landmarks above their noisy counterparts, providing
effective structural information for improved robustness. Evaluations on five
diverse datasets including MAFL, AFLW, LS3D, Cats, and Shoes demonstrate
excellent performance of the proposed approach compared to the existing
state-of-the-art methods. Our code is publicly available at
this https URL

我们通过引入一种基于图像重构的一致性引导瓶颈，在无监督的条件下探索目标标记的发现问题，利用标记的一致性测量与伪地面真值的兼容性得分来生成自适应热图，通过在图像间形成标记对应来获得伪监督，在自适应热图的生成中调节发现标记的不确定性，使得一致的标记高于嘈杂的标记，提供改进鲁棒性的有效结构信息，对 MAFL，AFLW，LS3D，Cats 和 Shoes 等五个不同数据集进行的评估表明，所提出的方法相比现有的最先进方法表现出色。