  Recent self-supervised models have demonstrated equal or better performance
than supervised methods, opening for AI systems to learn visual representations
from practically unlimited data. However, these methods are typically
classification-based and thus ineffective for learning high-resolution feature
maps that preserve precise spatial information. This work introduces
superpixels to improve self-supervised learning of dense semantically rich
visual concept embeddings. Decomposing images into a small set of visually
coherent regions reduces the computational complexity by $\mathcal{O}(1000)$
while preserving detail. We experimentally show that contrasting over regions
improves the effectiveness of contrastive learning methods, extends their
applicability to high-resolution images, improves overclustering performance,
superpixels are better than grids, and regional masking improves performance.
The expressiveness of our dense embeddings is demonstrated by improving the
SOTA unsupervised semantic segmentation benchmark on Cityscapes, and for
convolutional models on COCO.
