In this work, we introduce LEAD, an approach to discover 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 advances in
self-supervised learning of image features for instance-level tasks like
classification, these methods do not ensure dense equivariant representations.
The property of equivariance is of interest for dense prediction tasks like
landmark estimation. In this work, we introduce an approach to enhance the
learning of dense equivariant representations in a self-supervised fashion. We
follow a two-stage training approach: first, we train a network using the BYOL
objective which operates at an instance level. The correspondences obtained
through this network are further used to train a dense and compact
representation of the image using a lightweight network. We show that having
such a prior in the feature extractor helps in landmark detection, even under
drastically limited number of annotations while also improving generalization
across scale variations.

本文介绍了一种 LEAD 方法，用于从未注释的类别特定图像集合中发现地标。通过采用两阶段训练方法，该方法增强了自监督学习中密集等变表示的学习，并在极度有限的注释数下提高了地标检测的性能，同时提高了跨尺度变化的泛化能力。