论文标题
GSANET：具有全球和选择性关注的语义细分
GSANet: Semantic Segmentation with Global and Selective Attention
论文作者
论文摘要
本文提出了一种用于语义细分的新型深度学习体系结构。拟议的全球和选择性注意力网络（GSANET）具有具有新颖的Sparsemax全球注意力和新颖的选择性注意力，其凝结和扩散机制以汇总从提取的深度特征中汇总多尺度上下文信息。还提出了选择性注意解码器来处理GSA-ASPP输出，以优化软磁体积。我们是第一个使用低复杂性特征提取网络（FXN）MobileNetedge基准基准的语义分割网络的性能，该网络可针对边缘设备上的低延迟进行了优化。我们表明，GSANET可以通过Mobilenetedge以及强大的FXN（例如Xception）进行更精确的分割。 Gsanet提高了ADE20K和CityScapes数据集的最新语义细分精度。
This paper proposes a novel deep learning architecture for semantic segmentation. The proposed Global and Selective Attention Network (GSANet) features Atrous Spatial Pyramid Pooling (ASPP) with a novel sparsemax global attention and a novel selective attention that deploys a condensation and diffusion mechanism to aggregate the multi-scale contextual information from the extracted deep features. A selective attention decoder is also proposed to process the GSA-ASPP outputs for optimizing the softmax volume. We are the first to benchmark the performance of semantic segmentation networks with the low-complexity feature extraction network (FXN) MobileNetEdge, that is optimized for low latency on edge devices. We show that GSANet can result in more accurate segmentation with MobileNetEdge, as well as with strong FXNs, such as Xception. GSANet improves the state-of-art semantic segmentation accuracy on both the ADE20k and the Cityscapes datasets.
