DPANET:Dual Pooling Attention Network for Semantic Segmentation

TL;DR

This paper introduces DPANet, a lightweight neural network for semantic segmentation that uses dual pooling attention modules to efficiently capture contextual information with minimal parameters and computational complexity.

Contribution

The paper proposes a novel Dual Pool Attention Network (DPANet) with zero-parameter modules, significantly reducing complexity while maintaining effective semantic segmentation performance.

Findings

Demonstrates low parameter count and computational complexity.

Achieves effective segmentation results on benchmark datasets.

Validates the efficiency of dual pooling attention modules.

Abstract

Image segmentation is a historic and significant computer vision task. With the help of deep learning techniques, image semantic segmentation has made great progresses. Over recent years, based on guidance of attention mechanism compared with CNN which overcomes the problems of lacking of interaction between different channels, and effective capturing and aggregating contextual information. However, the massive operations generated by the attention mechanism lead to its extremely high complexity and high demand for GPU memory. For this purpose, we propose a lightweight and flexible neural network named Dual Pool Attention Network(DPANet). The most important is that all modules in DPANet generate \textbf{0} parameters. The first component is spatial pool attention module, we formulate an easy and powerful method densely to extract contextual characteristics and reduce the amount of calculation and complexity dramatically.Meanwhile, it demonstrates the power of even and large kernel size. The second component is channel pool attention module. It is known that the computation process of CNN incorporates the information of spatial and channel dimensions. So, the aim of this module is stripping them out, in order to construct relationship of all channels and heighten different channels semantic information selectively. Moreover, we experiments on segmentation datasets, which shows our method simple and effective with low parameters and calculation complexity.