  Contrastive learning methods for unsupervised visual representation learning
have reached remarkable levels of transfer performance. We argue that the power
of contrastive learning has yet to be fully unleashed, as current methods are
trained only on instance-level pretext tasks, leading to representations that
may be sub-optimal for downstream tasks requiring dense pixel predictions. In
this paper, we introduce pixel-level pretext tasks for learning dense feature
representations. The first task directly applies contrastive learning at the
pixel level. We additionally propose a pixel-to-propagation consistency task
that produces better results, even surpassing the state-of-the-art approaches
by a large margin. Specifically, it achieves 60.2 AP, 41.4 / 40.5 mAP and 77.2
mIoU when transferred to Pascal VOC object detection (C4), COCO object
detection (FPN / C4) and Cityscapes semantic segmentation using a ResNet-50
backbone network, which are 2.6 AP, 0.8 / 1.0 mAP and 1.0 mIoU better than the
previous best methods built on instance-level contrastive learning. Moreover,
the pixel-level pretext tasks are found to be effective for pre-training not
only regular backbone networks but also head networks used for dense downstream
tasks, and are complementary to instance-level contrastive methods. These
results demonstrate the strong potential of defining pretext tasks at the pixel
level, and suggest a new path forward in unsupervised visual representation
learning. Code is available at \url{https://github.com/zdaxie/PixPro}.
