论文标题
高阶配对ASPP网络用于语义segmenation
High-Order Paired-ASPP Networks for Semantic Segmenation
论文作者
论文摘要
当前的语义分割模型仅利用一阶统计数据，而很少探索高阶统计。但是，常见的一阶统计不足以支持坚实的一致表示。在本文中，我们提出了高阶配对网络，以利用各种特征级别的高阶统计数据。该网络首先引入高阶表示模块，以从主干的所有阶段提取上下文高阶信息。与一阶信息相比，它们可以提供更多的语义线索和歧视性信息。此外，提出了一个配对的ASPP模块将早期阶段的高阶统计数据嵌入最后阶段。它可以进一步保存在最终预测的低级特征中与边界相关和空间上下文。我们的实验表明，高阶统计数据显着提高了令人困惑的对象的性能。我们的方法在三个基准上，即CityScapes，Ade20k和Pascal-Context上的竞争性能，没有铃铛和哨声，MIOU为81.6％，45.3％和52.9％。
Current semantic segmentation models only exploit first-order statistics, while rarely exploring high-order statistics. However, common first-order statistics are insufficient to support a solid unanimous representation. In this paper, we propose High-Order Paired-ASPP Network to exploit high-order statistics from various feature levels. The network first introduces a High-Order Representation module to extract the contextual high-order information from all stages of the backbone. They can provide more semantic clues and discriminative information than the first-order ones. Besides, a Paired-ASPP module is proposed to embed high-order statistics of the early stages into the last stage. It can further preserve the boundary-related and spatial context in the low-level features for final prediction. Our experiments show that the high-order statistics significantly boost the performance on confusing objects. Our method achieves competitive performance without bells and whistles on three benchmarks, i.e, Cityscapes, ADE20K and Pascal-Context with the mIoU of 81.6%, 45.3% and 52.9%.
