CEDNet: A cascade encoder-decoder network for dense prediction

Gang Zhang, Ziyi Li, Chufeng Tang, Jianmin Li, Xiaolin Hu

Abstract: Highlights•We propose CEDNet, a cascade encoder–decoder network for dense prediction. A hallmark of CEDNet is its ability to incorporate high-level features from early stages to guide low-level feature learning in subsequent stages, thereby enhancing the effectiveness of multi-scale feature fusion.•We explored three well-known encoder–decoder structures: Hourglass, UNet, and FPN. They all performed much better than traditional methods that employ a pre-designed classification backbone combined with a lightweight multi-scale feature fusion module.•We conducted extensive experiments on object detection, instance segmentation, and semantic segmentation. The excellent performance we achieved on these tasks demonstrates the effectiveness of our method.