Feature Pyramid Network

Feature Pyramid Networks (FPNs) are a cornerstone of modern computer vision, designed to efficiently extract multi-scale features from images for tasks like object detection and segmentation. Current research focuses on enhancing FPN architectures through innovations such as attention mechanisms, improved feature fusion strategies (e.g., incorporating dilated convolutions or transformer-based approaches), and addressing challenges like class imbalance and computational cost. These advancements lead to improved accuracy and efficiency in diverse applications, including remote sensing, medical image analysis, and autonomous driving, impacting both scientific understanding and real-world problem-solving.