Multi-scale feature fusion is a technique in computer vision that combines features extracted at different scales. Since different scales of features can capture different levels of information, such as global context and local details, fusing them can help the model obtain more comprehensive information. This technique is widely used in various vision tasks, such as object detection and semantic segmentation, to improve the accuracy and robustness of the model.
