Multi-Pooling Context Network for Image Semantic Segmentation

With the development of image segmentation technology, image context information plays an increasingly important role in semantic segmentation. However, due to the complexity of context information in different feature maps, simple context capture operations can easily cause context information omission. Rich context information can better classify categories and improve the quality of image segmentation. On the contrary, poor context information will lead to blurred image category segmentation and an incomplete target edge. In order to capture rich context information as completely as possible, we constructed a Multi-Pooling Context Network (MPCNet), which is a multi-pool contextual network for the semantic segmentation of images. Specifically, we first proposed the Pooling Context Aggregation Module to capture the deep context information of the image by processing the information between the space, channel, and pixel of the image. At the same time, the Spatial Context Module was constructed to capture the detailed spatial context of images at different stages of the network. The whole network structure adopted the form of codec to better extract image context. Finally, we performed extensive experiments on three semantic segmentation datasets (Cityscapes, ADE20K, and PASCAL VOC2012 datasets), which fully proved that our proposed network effectively alleviated the lack of context extraction and verified the effectiveness of the network.

Automated summary

With the development of image segmentation technology, the importance of image context information in semantic segmentation has been recognized. In order to capture rich context information effectively, we proposed a Multi-Pooling Context Network (MPCNet) for image semantic segmentation. The network includes Pooling Context Aggregation Module and Spatial Context Module to capture deep context information and detailed spatial context respectively. Experimental results on multiple datasets demonstrate the effectiveness of our proposed network in context extraction.