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Mixed spatial pyramid pooling for semantic segmentation
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DOI：10.1016/j.asoc.2020.106209.png)
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Abstract
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Semantic segmentation is a challenging task as each pixel should be labeled accurately in the image. To improve the performance of semantic segmentation, some Fully Convolutional Network (FCN) based semantic segmentation methods adopt a spatial pyramid pooling structure to enrich contextual information. Others employ an encoder-decoder architecture to recover object details gradually. In this paper, we propose a semantic segmentation framework which combines the benefits of these approaches. Specifically, we propose a Mixed Spatial Pyramid Pooling (MSPP) module based on region-based average pooling and dilated convolution to obtain dense multi-level contextual priors. To further refine the details of objects more effectively, we also propose a Global-Attention Fusion (GAF) module to provide global context as guidance for low-level features. Our proposed method achieves mIoU of 84.1% on PASCAL VOC 2012 dataset and 80.4% on Cityscapes dataset without using any post-processing or additional datasets for pretrained model. (C) 2020 Elsevier B.V. All rights reserved.
Keywords:
Semantic segmentation
Convolutional neural network
Spatial pyramid pooling
Dilated convolution
Encoder-decoder architecture
Scene understanding
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