Sparse Spatial Attention Network for Semantic Segmentation

• URL: http://arxiv.org/abs/2109.01915v1
• Date: Sat, 4 Sep 2021 18:41:05 GMT
• Title: Sparse Spatial Attention Network for Semantic Segmentation
• Authors: Mengyu Liu and Hujun Yin
• Abstract summary: The spatial attention mechanism captures long-range dependencies by aggregating global contextual information to each query location. We present a sparse spatial attention network (SSANet) to improve the efficiency of the spatial attention mechanism without sacrificing the performance.
• Score: 11.746833714322156
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The spatial attention mechanism captures long-range dependencies by aggregating global contextual information to each query location, which is beneficial for semantic segmentation. In this paper, we present a sparse spatial attention network (SSANet) to improve the efficiency of the spatial attention mechanism without sacrificing the performance. Specifically, a sparse non-local (SNL) block is proposed to sample a subset of key and value elements for each query element to capture long-range relations adaptively and generate a sparse affinity matrix to aggregate contextual information efficiently. Experimental results show that the proposed approach outperforms other context aggregation methods and achieves state-of-the-art performance on the Cityscapes, PASCAL Context and ADE20K datasets.

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