Contrastive self-supervised learning has emerged as a promising approach to
unsupervised visual representation learning. In general, these methods learn
global (image-level) representations that are invariant to different views
(i.e., compositions of data augmentation) of the same image. However, many
visual understanding tasks require dense (pixel-level) representations. In this
paper, we propose View-Agnostic Dense Representation (VADeR) for unsupervised
learning of dense representations. VADeR learns pixelwise representations by
forcing local features to remain constant over different viewing conditions.
Specifically, this is achieved through pixel-level contrastive learning:
matching features (that is, features that describes the same location of the
scene on different views) should be close in an embedding space, while
non-matching features should be apart. VADeR provides a natural representation
for dense prediction tasks and transfers well to downstream tasks. Our method
outperforms ImageNet supervised pretraining (and strong unsupervised baselines)
in multiple dense prediction tasks.

本文提出了一种名为 VADeR 的无监督学习方法，通过像素级对比学习来学习像素级别的视图无关的密集表示，该方法在多个密集预测任务中优于 ImageNet 监督预训练和强无监督基线。