Feature Pyramid Network
Feature Pyramid Network (FPN) is a deep learning architecture designed for multi-scale feature extraction in computer vision tasks, particularly object detection and segmentation. It builds a feature pyramid with strong semantics at all scales by combining low-resolution, semantically strong features with high-resolution, semantically weak features through a top-down pathway and lateral connections. This enables the network to detect objects of various sizes more accurately and efficiently than traditional single-scale approaches.
Developers should learn FPN when working on computer vision applications that require robust object detection across different scales, such as autonomous driving, medical imaging, or surveillance systems. It addresses the limitation of standard convolutional networks that lose spatial resolution at deeper layers, making it essential for tasks like instance segmentation in Mask R-CNN or object detection in Faster R-CNN, where detecting small objects is critical.