Convolution pyramid attention: an efficient channel attention mechanism
Ruitong Wang, Xiangju Jiang · 2024
Many studies show that convolutional neural networks can improve performance after embedding attention mechanism. However, the existing related research either develops more complex attention modules in pursuit of ultimate performance improvement; Or the performance improvement is not obvious in pursuit of ultra-lightweight In order to balance the contradiction between the performance and complexity of attention mechanism, this paper proposes a simple convolution pyramid attention module (CPA). Firstly, the module constructs a convolution pyramid which can be used for multi-scale feature extraction by stacking two-dimensional convolution with convolution kernel size of 1, and then counts the global information of feature mapping in the channel dimension. Then, the feature fusion is carried out with the exclusion block, and the output weight vector is multiplied with the original feature mapping in the channel dimension, thus realizing the adaptive correction of feature mapping in the channel dimension. Finally, the performance superiority of the proposed attention mechanism is proved by image classification and target detection tasks, and then its structural rationality is proved by ablation experiments.