  In this work, we present Multi-Level Contrastive Learning for Dense
Prediction Task (MCL), an efficient self-supervised method for learning
region-level feature representation for dense prediction tasks. Our method is
motivated by the three key factors in detection: localization, scale
consistency and recognition. To explicitly encode absolute position and scale
information, we propose a novel pretext task that assembles multi-scale images
in a montage manner to mimic multi-object scenarios. Unlike the existing
image-level self-supervised methods, our method constructs a multi-level
contrastive loss that considers each sub-region of the montage image as a
singleton. Our method enables the neural network to learn regional semantic
representations for translation and scale consistency while reducing
pre-training epochs to the same as supervised pre-training. Extensive
experiments demonstrate that MCL consistently outperforms the recent
state-of-the-art methods on various datasets with significant margins. In
particular, MCL obtains 42.5 AP$^\mathrm{bb}$ and 38.3 AP$^\mathrm{mk}$ on COCO
with the 1x schedule fintuning, when using Mask R-CNN with R50-FPN backbone
pre-trained with 100 epochs. In comparison to MoCo, our method surpasses their
performance by 4.0 AP$^\mathrm{bb}$ and 3.1 AP$^\mathrm{mk}$. Furthermore, we
explore the alignment between pretext task and downstream tasks. We extend our
pretext task to supervised pre-training, which achieves a similar performance
to self-supervised learning. This result demonstrates the importance of the
alignment between pretext task and downstream tasks, indicating the potential
for wider applicability of our method beyond self-supervised settings.
