LEAD: Self-Supervised Landmark Estimation by Aligning Distributions of Feature Similarity
LEAD: Self-Supervised Landmark Estimation by Aligning Distributions of Feature Similarity
In this work, we introduce LEAD, an approach to dis-cover landmarks from an unannotated collection of category-specific images. Existing works in self-supervised landmark detection are based on learning dense (pixel-level) feature representations from an image, which are further used to learn landmarks in a semi-supervised manner. While there have been …