Oct 9, 2024 · FPNs create a pyramid of features that lets the network examine both high-level (bigger objects) and low-level (smaller objects) information, ensuring that no detail is left behind.

A Feature Pyramid Network, or FPN, is a feature extractor that takes a single-scale image of an arbitrary size as input, and outputs proportionally sized feature maps at multiple levels, in a …

By leveraging both high- and low-level feature maps in a structured way, FPN achieves a robust multi-scale representation that significantly enhances detection accuracy. Here’s a breakdown …

Dec 9, 2016 · Using FPN in a basic Faster R-CNN system, our method achieves state-of-the-art single-model results on the COCO detection benchmark without bells and whistles, surpassing …

Sep 9, 2020 · FPN solves this problem by generating a bottom-up and a top-down feature hierarchy with lateral connections from the network’s generated features at different scales.

Jan 14, 2025 · Among these innovations, Feature Pyramid Networks (FPN) stand out as a fundamental building block that has revolutionized how we handle multi-scale feature …

Feature Pyramid Network (FPN) is an architecture designed to enhance object detection by addressing scale variation challenges in images. It constructs a feature pyramid with high-level …

Dec 20, 2019 · In this paper, we first investigate the Feature Pyramid Network (FPN) architectures and briefly categorize them into three typical fashions: top-down, bottom-up and fusing …

FPN takes a single-scale image of an arbitrary input size and outputs proportionally sized feature maps at multiple levels. FPN is independent of backbone networks (ResNet is used in the …

In this work, we propose Attention Aggre-gation based Feature Pyramid Network (A2-FPN), to im-prove multi-scale feature learning through attention-guided feature aggregation.