# Feature Pyramid Grids

arXiv: https://arxiv.org/abs/2004.03580 · HTML: https://mlsanity.com/paper/2004.03580/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Apr 2020.

## Built on by (most cited first, 13 of 13)

- [RTMDet: An Empirical Study of Designing Real-Time Object Detectors](https://mlsanity.com/paper/2212.07784/) (2022)
- [PIDray: A Large-scale X-ray Benchmark for Real-World Prohibited Item Detection](https://mlsanity.com/paper/2211.10763/) (2022)
- [Centralized Feature Pyramid for Object Detection](https://mlsanity.com/paper/2210.02093/) (2022)
- [Learning to Aggregate Multi-Scale Context for Instance Segmentation in Remote Sensing Images](https://mlsanity.com/paper/2111.11057/) (2021)
- [Cross-Layer Feature Pyramid Transformer for Small Object Detection in Aerial Images](https://mlsanity.com/paper/2407.19696/) (2024)
- [Boosting R-CNN: Reweighting R-CNN Samples by RPN's Error for Underwater Object Detection](https://mlsanity.com/paper/2206.13728/) (2022)
- [Excavating RoI Attention for Underwater Object Detection](https://mlsanity.com/paper/2206.12128/) (2022)
- [SDTP: Semantic-aware Decoupled Transformer Pyramid for Dense Image Prediction](https://mlsanity.com/paper/2109.08963/) (2021)
- [Transformer-based Context Condensation for Boosting Feature Pyramids in Object Detection](https://mlsanity.com/paper/2207.06603/) (2022)
- [Application of Ghost-DeblurGAN to Fiducial Marker Detection](https://mlsanity.com/paper/2109.03379/) (2021)
- [RiWNet: A moving object instance segmentation Network being Robust in adverse Weather conditions](https://mlsanity.com/paper/2109.01820/) (2021)
- [The Untapped Potential of Off-the-Shelf Convolutional Neural Networks](https://mlsanity.com/paper/2103.09891/) (2021)
- [PADetBench: Towards Benchmarking Physical Attacks against Object Detection](https://mlsanity.com/paper/2408.09181/) (2024)

## Abstract (arXiv)

Feature pyramid networks have been widely adopted in the object detection literature to improve feature representations for better handling of variations in scale. In this paper, we present Feature Pyramid Grids (FPG), a deep multi-pathway feature pyramid, that represents the feature scale-space as a regular grid of parallel bottom-up pathways which are fused by multi-directional lateral connections. FPG can improve single-pathway feature pyramid networks by significantly increasing its performance at similar computation cost, highlighting importance of deep pyramid representations. In addition to its general and uniform structure, over complicated structures that have been found with neural architecture search, it also compares favorably against such approaches without relying on search. We hope that FPG with its uniform and effective nature can serve as a strong component for future work in object recognition.