  This work considers supervised contrastive learning for semantic
segmentation. We apply contrastive learning to enhance the discriminative power
of the multi-scale features extracted by semantic segmentation networks. Our
key methodological insight is to leverage samples from the feature spaces
emanating from multiple stages of a model's encoder itself requiring neither
data augmentation nor online memory banks to obtain a diverse set of samples.
To allow for such an extension we introduce an efficient and effective sampling
process, that enables applying contrastive losses over the encoder's features
at multiple scales. Furthermore, by first mapping the encoder's multi-scale
representations to a common feature space, we instantiate a novel form of
supervised local-global constraint by introducing cross-scale contrastive
learning linking high-resolution local features to low-resolution global
features. Combined, our multi-scale and cross-scale contrastive losses boost
performance of various models (DeepLabV3, HRNet, OCRNet, UPerNet) with both CNN
and Transformer backbones, when evaluated on 4 diverse datasets from natural
(Cityscapes, PascalContext, ADE20K) but also surgical (CaDIS) domains. Our code
is available at https://github.com/RViMLab/MS_CS_ContrSeg. datasets from
natural (Cityscapes, PascalContext, ADE20K) but also surgical (CaDIS) domains.
