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Multi-scale and Cross-scale Contrastive Learning for Semantic Segmentation
[article]
2022
arXiv
pre-print
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
arXiv:2203.13409v2
fatcat:dghhmealjjhffagwuol5moah6m