Pyramid Fusion

Pyramid fusion is a technique in deep learning that combines multi-scale features extracted from neural networks to improve the accuracy and efficiency of various tasks. Current research focuses on optimizing feature fusion strategies within pyramid architectures, employing methods like dense spatial pyramid pooling and optimal transport, and integrating these with diverse backbone networks such as Feature Pyramid Networks (FPNs), Swin Transformers, and ConvNeXts. This approach has demonstrated significant performance gains across diverse applications, including medical image segmentation, time series forecasting, and semantic segmentation of remote sensing imagery and 3D scenes, showcasing its broad utility in computer vision and related fields.