.png){kind=icon}
.png){kind=icon}
Return
Bilateral attention decoder: A lightweight decoder for real-time semantic segmentation
.png){kind=icon}
PRE
AI
DOI：10.1016/j.neunet.2021.01.021.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The encoder-decoder structure has been introduced into semantic segmentation to improve the spatial accuracy of the network by fusing high- and low-level feature maps. However, recent state-of-the-art encoder-decoder-based methods can hardly attain the real-time requirement due to their complex and inefficient decoders. To address this issue, in this paper, we propose a lightweight bilateral attention decoder for real-time semantic segmentation. It consists of two blocks and can fuse different level feature maps via two steps, i.e., information refinement and information fusion. In the first step, we propose a channel attention branch to refine the high-level feature maps and a spatial attention branch for the low-level ones. The refined high-level feature maps can capture more exact semantic information and the refined low-level ones can capture more accurate spatial information, which significantly improves the information capturing ability of these feature maps. In the second step, we develop a new fusion module named pooling fusing block to fuse the refined high- and low-level feature maps. This fusion block can take full advantages of the high- and low-level feature maps, leading to high-quality fusion results. To verify the efficiency of the proposed bilateral attention decoder, we adopt a lightweight network as the backbone and compare our proposed method with other state-of-the-art real-time semantic segmentation methods on the Cityscapes and Camvid datasets. Experimental results demonstrate that our proposed method can achieve better performance with a higher inference speed. Moreover, we compare our proposed network with several state-of-the-art non-real-time semantic segmentation methods and find that our proposed network can also attain better segmentation performance. (C) 2021 Elsevier Ltd. All rights reserved.
Keywords:
Semantic segmentation
Real time
Deep learning
Attention mechanism
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
6.3
Papers:
7.8K
Citations:
3.0W
Organization
View more organizations