We propose a structure called stride spatial pyramid pooling (SSPP) to capture multiscale semantic information from the high-level feature map.

Dec 31, 2019 · We propose a new decoder to make full use of the low- and high-level feature maps.•Auxiliary loss is applied to make the network easier to train ...

Semantic segmentation based on double pyramid network with improved global attention mechanism · Multi-scale dense and attention mechanism for image semantic ...

Semantic segmentation is an end-to-end task that requires both semantic and spatial accuracy. It is important for deep learning-based segmentation methods ...

The dual attention decoder can result in a more “semantic” low-level feature map and a high-level feature map with more accurate spatial information, which ...

Feb 14, 2023 · So, this paper proposes a global attention double pyramid network(GADPNet) based on an improved global attention mechanism to improve the ...

Nov 2, 2020 · Data from Wuhan University Provide New Insights into Pattern Analysis (Semantic Segmentation Using Stride Spatial Pyramid Pooling and Dual ...

This work introduces a Feature Pyramid Attention module to perform spatial pyramid attention structure on high-level output and combining global pooling.

We propose a novel and lightweight network with a dual encoder and self-attention module for real-time semantic segmentation in the field of autonomous driving;.

Feb 14, 2023 · Pyramid decoder structure can take advantage of multi-scale features from ASPP module and different stages' low-level multi-scale feature maps ...