Combining Soft and Hard Attentions for high-quality single-stage instance segmentation
Qiong Chen, Yaochi Zhao, Yujia Chen, He Zhang, Zhuhua Hu · 2024
The existing single-stage instance segmentation networks achieve real-time segmentation by using simple convolutional networks and limiting the number of feature scales, which greatly limits the performance of model. To solve this problem, we explore the trade-off between performance and speed from the aspects of feature extraction and training strategy. First, we propose to combine soft and hard attentions (CSHA) in a lightweight module, which can improve the attention allocation ability of model for channels and enhance the awareness ability of important features. Furthermore, we employ serial and parallel feature fusion (SPFF) module, which can increase the diversity of feature scales. Moreover, we employ Poly Focal Loss (PFL) to improve the model performance without any additional costs during inference. Our method can reduce the missing detection rate and increase the AP value to 36.1% on COCO dataset, maintaining real-time instance segmentation, which exceeds the current mainstream methods.