Feature Pyramid Network
Feature pyramids are a basic component for detecting objects on different scales. Before this paper, a lot of research has been avoiding these pyramid structures due to their high computational and memory costs. Feature Pyramid Network tackles this challenge.
By proposing a top-down architecture with lateral connections, it has successfully built high-level semantic feature maps at all scales, leading to higher accuracy while efficiently reusing past calculation results.
