Sensors (Jun 2022)

Multiple-Attention Mechanism Network for Semantic Segmentation

  • Dongli Wang,
  • Shengliang Xiang,
  • Yan Zhou,
  • Jinzhen Mu,
  • Haibin Zhou,
  • Richard Irampaye

DOI
https://doi.org/10.3390/s22124477
Journal volume & issue
Vol. 22, no. 12
p. 4477

Abstract

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Contextual information and the dependencies between dimensions is vital in image semantic segmentation. In this paper, we propose a multiple-attention mechanism network (MANet) for semantic segmentation in a very effective and efficient way. Concretely, the contributions are as follows: (1) a novel dual-attention mechanism for capturing feature dependencies in spatial and channel dimensions, where the adjacent position attention captures the dependencies between pixels well; (2) a new cross-dimensional interactive attention feature fusion module, which strengthens the fusion of fine location structure information in low-level features and category semantic information in high-level features. We conduct extensive experiments on semantic segmentation benchmarks including PASCAL VOC 2012 and Cityscapes datasets. Our MANet achieves the mIoU scores of 75.5% and 72.8% on PASCAL VOC 2012 and Cityscapes datasets, respectively. The effectiveness of the network is higher than the previous popular semantic segmentation networks under the same conditions.

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