Contextrast: Contextual Contrastive Learning for Semantic Segmentation
Changki Sung Wanhee Kim Jungho An Wooju Lee Hyungtae Lim Hyun Myung
Despite great improvements in semantic segmentation, challenges persist
because of the lack of local/global contexts and the relationship between them.
In this paper, we propose Contextrast, a contrastive learning-based semantic
segmentation method that allows to capture local/global contexts and comprehend
their relationships. Our proposed method comprises two parts: a) contextual
contrastive learning (CCL) and b) boundary-aware negative (BANE) sampling.
Contextual contrastive learning obtains local/global context from multi-scale
feature aggregation and inter/intra-relationship of features for better
discrimination capabilities. Meanwhile, BANE sampling selects embedding
features along the boundaries of incorrectly predicted regions to employ them
as harder negative samples on our contrastive learning, resolving segmentation
issues along the boundary region by exploiting fine-grained details. We
demonstrate that our Contextrast substantially enhances the performance of
semantic segmentation networks, outperforming state-of-the-art contrastive
learning approaches on diverse public datasets, e.g. Cityscapes, CamVid,
PASCAL-C, COCO-Stuff, and ADE20K, without an increase in computational cost
during inference.