语义分割作完成像素级的分类任务,上下文信息对分割的性能有重要的影响.为了获取更丰富的上下文信息,采用ResNet作为主干网络,设计了一个基于多维度注意模块(Multidimensional attention,MDA)和多尺度上采样模块(Multiscale upsampling,MSU)的编码器-解码器结构.多维度注意力模块计算三个维度的注意力矩阵,以获取每个位置的依赖性,同时注意力机制能自适应地捕捉图像特征.多尺度上采样模块采用并行分支来捕获图像的多尺度特征,多尺度特征聚合有效地增强了图像的上下文信息.在Cityscapes和Camvid数据集上进行的一系列实验表明,该网络能有效提升图像分割精度.“,”Semantic segmentation is for pixel-level classification tasks,and contextual information has an important impact on the performance of segmentation.In order to capture richer contextual information,we adopt ResNet as the backbone network and designs an encoder-decoder architecture based on multidimensional attention(MDA)module and multiscale upsampling(MSU)module.The MDA module calculates the attention matrices of the three dimensions to capture the dependency of each position,and adaptively captures the image features.The MSU module adopts parallel branches to capture the multiscale features of the images,and multiscale feature aggregation can enhance contextual information.A series of experiments demonstrate the validity of the model on Cityscapes and Camvid datasets.