Learning to Predict Context-adaptive Convolution for Semantic Segmentation

• URL: http://arxiv.org/abs/2004.08222v2
• Date: Wed, 26 Aug 2020 03:49:13 GMT
• Title: Learning to Predict Context-adaptive Convolution for Semantic Segmentation
• Authors: Jianbo Liu, Junjun He, Jimmy S. Ren, Yu Qiao, Hongsheng Li
• Abstract summary: Long-range contextual information is essential for achieving high-performance semantic segmentation. We propose a Context-adaptive Convolution Network (CaC-Net) to predict a spatially-varying feature weighting vector. Our CaC-Net achieves superior segmentation performance on three public datasets.
• Score: 66.27139797427147
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Long-range contextual information is essential for achieving high-performance semantic segmentation. Previous feature re-weighting methods demonstrate that using global context for re-weighting feature channels can effectively improve the accuracy of semantic segmentation. However, the globally-sharing feature re-weighting vector might not be optimal for regions of different classes in the input image. In this paper, we propose a Context-adaptive Convolution Network (CaC-Net) to predict a spatially-varying feature weighting vector for each spatial location of the semantic feature maps. In CaC-Net, a set of context-adaptive convolution kernels are predicted from the global contextual information in a parameter-efficient manner. When used for convolution with the semantic feature maps, the predicted convolutional kernels can generate the spatially-varying feature weighting factors capturing both global and local contextual information. Comprehensive experimental results show that our CaC-Net achieves superior segmentation performance on three public datasets, PASCAL Context, PASCAL VOC 2012 and ADE20K.

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