Contextrast：面向语义分割的上下文对比学习
Contextrast: Contextual Contrastive Learning for Semantic Segmentation
AI总结：
针对语义分割中局部/全局上下文及其关联缺失的问题，提出含上下文对比学习与边界感知负样本采样的Contextrast方法，在多数据集上以相同推理成本超越现有对比学习SOTA。
摘要：
尽管语义分割已取得显著进展，但由于缺乏局部/全局上下文以及二者之间的关联，相关挑战依然存在。本文提出Contextrast——一种基于对比学习的语义分割方法，能够捕捉局部/全局上下文并理解其关联。该方法包含两部分：a）上下文对比学习（CCL）和b）边界感知负样本（BANE）采样。上下文对比学习通过多尺度特征聚合以及特征的层间/层内关联获取局部/全局上下文，以提升判别能力。同时，BANE采样选取错误预测区域边界处的嵌入特征，作为对比学习中更难的负样本，利用细粒度细节解决边界区域的分割问题。实验证明，Contextrast大幅提升了语义分割网络的性能，在Cityscapes、CamVid、PASCAL-C、COCO-Stuff和ADE20K等多个公开数据集上优于现有最先进的对比学习方法，且推理阶段计算成本未增加。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
