Realtime Global Attention Network for Semantic Segmentation

• URL: http://arxiv.org/abs/2112.12939v1
• Date: Fri, 24 Dec 2021 04:24:18 GMT
• Title: Realtime Global Attention Network for Semantic Segmentation
• Authors: Xi Mo, Xiangyu Chen
• Abstract summary: We propose an integrated global attention neural network (RGANet) for semantic segmentation. The integration of these global attention modules into a hierarchy of transformations maintains an improved evaluation metric performance.
• Score: 4.061739586881057
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we proposed an end-to-end realtime global attention neural network (RGANet) for the challenging task of semantic segmentation. Different from the encoding strategy deployed by self-attention paradigms, the proposed global attention module encodes global attention via depth-wise convolution and affine transformations. The integration of these global attention modules into a hierarchy architecture maintains high inferential performance. In addition, an improved evaluation metric, namely MGRID, is proposed to alleviate the negative effect of non-convex, widely scattered ground-truth areas. Results from extensive experiments on state-of-the-art architectures for semantic segmentation manifest the leading performance of proposed approaches for robotic monocular visual perception.

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