Before revision:  In this paper, we utilize a linearized attention function to overcome above problems and build a novel architecture , named Content-Augmented Feature Pyramid Network (CA-FPN) , which proposes a global content extraction module and deeply combines with FPN through light linear transformers. What's more, light transformers can further make the application of multi-head attention mechanism easier. Most importantly, our CA-FPN can be readily plugged into existing FPN-based models.
After revision:  In this paper, we utilize a linearized attention function to overcome above problems and build a novel architecture , named Content-Augmented Feature Pyramid Network (CA-FPN) , which proposes a global content extraction module and light linear spatial transformers. The former allows to extract multi-scale context information and the latter can deeply combine the global content extraction module with the vanilla FPN using the linearized attention function, which is designed to reduce model complexity. Furthermore, CA-FPN can be readily plugged into existing FPN-based models.