Improving the efficiency of state-of-the-art methods in semantic segmentation requires overcoming the increasing computational cost as well as issues such as fusing semantic information from global and local contexts. Based on the recent success and problems that convolutional neural networks (CNNs) encounter in semantic segmentation, this research proposes an encoder-decoder architecture with a unique efficient residual network, Efficient-ResNet. Attention-boosting gates (AbGs) and attention-boosting modules (AbMs) are deployed by aiming to fuse the equivariant and feature-based semantic information with the equivalent sizes of the output of global context of the efficient residual network in the encoder. Respectively, the decoder network is developed with the additional attention-fusion networks (AfNs) inspired by AbM. AfNs are designed to improve the efficiency in the one-to-one conversion of the semantic information by deploying additional convolution layers in the decoder part. Our network is tested on the challenging CamVid and Cityscapes datasets, and the proposed methods reveal significant improvements on the residual networks. To the best of our knowledge, the developed network, SERNet-Former, achieves state-of-the-art results (84.62 % mean IoU) on CamVid dataset and challenging results (87.35 % mean IoU) on Cityscapes validation dataset.

翻译：提升语义分割前沿方法的效率，需要克服计算成本不断增长的问题，以及融合全局与局部上下文语义信息等挑战。基于卷积神经网络（CNN）在语义分割中近年取得的成功及面临的问题，本研究提出了一种编码器-解码器架构，其核心为独特的高效残差网络Efficient-ResNet。通过在编码器中部署注意力增强门控（AbG）与注意力增强模块（AbM），旨在融合等变特征与基于特征的语义信息，并与高效残差网络全局上下文输出的等效尺寸相匹配。相应地，受AbM启发，解码器网络额外开发了注意力融合网络（AfN），通过在解码部分部署额外的卷积层，提升语义信息一对一转换的效率。本网络在具有挑战性的CamVid和Cityscapes数据集上进行了测试，所提出的方法在残差网络上展现出显著改进。据我们所知，所开发的SERNet-Former网络在CamVid数据集上取得了当前最优结果（平均交并比84.62%），并在Cityscapes验证数据集上取得了具有挑战性的结果（平均交并比87.35%）。