Locating semantically meaningful landmark points is a crucial component of a
large number of computer vision pipelines. Because of the small number of
available datasets with ground truth landmark annotations, it is important to
design robust unsupervised and semi-supervised methods for landmark detection.
Many of the recent unsupervised learning methods rely on the equivariance
properties of landmarks to synthetic image deformations. Our work focuses on
such widely used methods and sheds light on its core problem, its inability to
produce equivariant intermediate convolutional features. This finding leads us
to formulate a two-step unsupervised approach that overcomes this challenge by
first learning powerful pixel-based features and then use the pre-trained
features to learn a landmark detector by the traditional equivariance method.
Our method produces state-of-the-art results in several challenging landmark
detection datasets such as the BBC Pose dataset and the Cat-Head dataset. It
performs comparably on a range of other benchmarks.

本文研究了关键点检测中使用的无监督学习方法，重点介绍了其不能生成等变中间卷积特征的问题，并提出了一种两步无监督方法，该方法通过首先学习强大的基于像素的特征，然后使用预训练的特征来通过传统的等变性方法学习关键点检测器。在多个挑战性的标志性检测数据集上实现了最先进的结果，例如 BBC Pose 数据集和 Cat-Head 数据集，并在一系列基准测试中表现出色。