Laser & Optoelectronics Progress, Volume. 58, Issue 14, 1410012(2021)
Lightweight Semantic Segmentation Network Based on Attention Coding
To address the issues of high computational complexity and large memory footprint of the attention map of the self-attention mechanism and to improve the performance of the semantic segmentation network, we propose a lightweight network based on attention coding. The network uses an adaptive positional attention module and global attention upsampling module to encode and decode long-range dependency information, respectively. When calculating the attention map, adaptive positional attention module excludes useless basis sets and context information is obtained. A global attention upsampling module uses global context information to guide low-level features to reconstruct high-resolution images. Experimental results show that the segmentation accuracy of the network on the PASCAL VOC2012 verification set reaches a value of 84.9%. Compared with dual attention network, which has a similar segmentation accuracy, the giga floating-point operations per second and the GPU memory of the network are reduced by 16.9% and 12.9%, respectively.
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Xiaolong Chen, Ji Zhao, Siyi Chen. Lightweight Semantic Segmentation Network Based on Attention Coding[J]. Laser & Optoelectronics Progress, 2021, 58(14): 1410012
Category: Image Processing
Received: Sep. 16, 2020
Accepted: Nov. 14, 2020
Published Online: Jun. 30, 2021
The Author Email: Chen Xiaolong (350071235@qq.com), Chen Siyi (c.siyi@xtu.edu.cn)