Yet again simple approach leading to unsupervised segmentation. Mostly useful as pre-training though.

Proposed pipeline first mines saliency object areas (with any available framework, possibly supervised) and then makes contrast learning for pixel embeddings inside those regions. During second step individual pixel embedding is attracted to the mean embedding of its object and pushed away from mean embeddings of other objects. This additional detail differs it from some previously proposed pipelines and allows wider training, because of slower growing rate of the loss pairs.

Less briefly and with some external links here.
Source here.