  While self-supervised learning has been shown to benefit a number of vision
tasks, existing techniques mainly focus on image-level manipulation, which may
not generalize well to downstream tasks at patch or pixel levels. Moreover,
existing SSL methods might not sufficiently describe and associate the above
representations within and across image scales. In this paper, we propose a
Self-Supervised Pyramid Representation Learning (SS-PRL) framework. The
proposed SS-PRL is designed to derive pyramid representations at patch levels
via learning proper prototypes, with additional learners to observe and relate
inherent semantic information within an image. In particular, we present a
cross-scale patch-level correlation learning in SS-PRL, which allows the model
to aggregate and associate information learned across patch scales. We show
that, with our proposed SS-PRL for model pre-training, one can easily adapt and
fine-tune the models for a variety of applications including multi-label
classification, object detection, and instance segmentation.
