Efficient Feature Recombining Network Based on Refining Multi-Level Feature Maps for Semantic Segmentation

Modern approaches for semantic segmentation usually concatenate the feature map of the last convolutional layer and multi-scale features to form the final feature representation, which can achieve the more accurate classification of target pixels for the input image. However, the feature information of the last layer is not complete and refined so that there is a performance bottleneck in the concatenation between the final feature map and multi-scale feature representations. To solve this problem, we propose the Feature Recombining Network to get more refined and precise features for Semantic Segmentation. Our network is composed of Feature Recombining Module and Modified Pyramid Pooling Module. The two modules can extract more detailed and representative features through the feature recombination and acquire richer context information than the previous module respectively. Experiments show that our modules are effective to improve the segmentation precision and the Modified Pyramid Pooling Module is also superior to the previous module. Based on our proposed network, we achieve the performance of 51.9% mIoU on Pascal Context dataset and 44.75% mIoU on ADE20K dataset.