Given a collection of images, humans are able to discover landmarks by
modeling the shared geometric structure across instances. This idea of
geometric equivariance has been widely used for the unsupervised discovery of
object landmark representations. In this paper, we develop a simple and
effective approach by combining instance-discriminative and
spatially-discriminative contrastive learning. We show that when a deep network
is trained to be invariant to geometric and photometric transformations,
representations emerge from its intermediate layers that are highly predictive
of object landmarks. Stacking these across layers in a "hypercolumn" and
projecting them using spatially-contrastive learning further improves their
performance on matching and few-shot landmark regression tasks. We also present
a unified view of existing equivariant and invariant representation learning
approaches through the lens of contrastive learning, shedding light on the
nature of invariances learned. Experiments on standard benchmarks for landmark
learning, as well as a new challenging one we propose, show that the proposed
approach surpasses prior state-of-the-art.

该论文提出了一种将基于实例和空间差异的对比学习相结合的简单而有效的方法，在训练的中间层次中表示对象明显地预测对象地标，通过空间对比学习进一步提高匹配和少量样本标志物回归任务的性能。该方法优于现有同类方法，并在标志物学习的标准基准测试以及该论文提出的新挑战上得到了验证。