  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.
