Pooling Attention-based Encoder-Decoder Network for semantic segmentation

Aiming to the challenge of poor pixel-consistency in inter-category and pixel-similarity in inter-category, in this paper, we propose an Encoder-Decoder network for image semantic segmentation using pooling SE-ResNet attention module, called PAEDN. It is an effective of attention mechanism to get aggregated information. According to the principle of SE-ResNet, a collection of Average, Maximum and Stochastic global pooling, which concentrate on contoured, detailed, and generalized information in a certain semantic segmentation, form attention modules. Channel Pooling Attention Module (CPAM) and Position Pooling Attention Module (PPAM) are designed and integrated into the Encoder to extract discriminative features from input images, and the Decoder is developed through SE-ResNet attention module to fuse the feature map in high-resolution with that in low-resolution. Experimental evaluations performed on the data sets PASCAL and Cityscapes, show the proposed Encoder-Decoder with pooling attention module produces good pixel-consistency semantic label, achieves 15.1% improvement to FCN.

Automated summary

This paper proposes an Encoder-Decoder network for image semantic segmentation using pooling SE-ResNet attention module, called PAEDN, to address the challenge of poor pixel-consistency in inter-category and pixel-similarity in inter-category. Experimental evaluations on PASCAL and Cityscapes datasets show that the proposed method achieves good pixel-consistency semantic label and a 15.1% improvement over FCN.