Journal
IEEE ACCESS
Volume 7, Issue -, Pages 173679-173693Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2019.2956216
Keywords
Semantic segmentation; pyramid context contrast; subpixel convolution; dense decoder; boundary refinement
Categories
Funding
- National Natural Science Foundation of China [61672158, 61972097, 61502105, 61672159, 61502104]
- Industry-Academy Cooperation Project [2018H6010]
- Technology Guidance Project of Fujian Province [2017H0015]
- Fujian Collaborative Innovation Center for Big Data Application in Governments
Ask authors/readers for more resources
Semantic segmentation plays a critical role in image understanding. Recently, Fully Convolutional Network (FCN)-based models have made significant progress in semantic segmentation. However, achieving the full utilization of contextual information and recovery of lost spatial details remains a huge challenge. In this paper, we present a semantic segmentation model based on pyramid context contrast and a subpixel-aware dense decoder. We propose first using the pyramid context contrast to exploit the capability of contextual information by aggregating multi-scale foreground representations in different background regions via the pyramid context contrast module. Then, we add a subpixel-aware dense decoder architecture to reuse features extracted from different decoder levels by pixel shuffle, which can reasonably resolve resolution inconsistency between feature maps. Next, we refine the boundary by utilizing spatial visual information about low-level features via a boundary refinement branch with addition of auxiliary supervision. The presented model was evaluated using the PASCAL VOC 2012 semantic segmentation benchmark and achieved a performance of 86.9%, demonstrating that the proposed model achieves considerable improvement over most state-of-the-art models.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available