The Novel Instance Segmentation Method Based on Multi-Level Features and Joint Attention

Instance segmentation is an important task in computer vision. In order to enhance the multi-level features expression ability of the segmentation networks, a novel module is proposed in this paper. Firstly, we design a weighted bi-directional feature fusion way by computing the weight distribution function of bi-directional feature pyramid network. Secondly, we propose a joint attention mechanism to effectively filter different levels of feature information by adopting serial and parallel ways to combine the channel attention and spatial attention modules. At the same time, the module uses dynamic convolution to stabilize the calculation speed while improve the 6.7% mean average precision of segmentation. The experiments on the COCO dataset demonstrate that the module can effectively improve the performance of the existing instance segmentation networks.