  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
https://github.com/MamonaAwan/CGB_ULD.
