# A novel Region of Interest Extraction Layer for Instance Segmentation

arXiv: https://arxiv.org/abs/2004.13665 · HTML: https://mlsanity.com/paper/2004.13665/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Apr 2020.

## Built on by (most cited first, 17 of 17)

- [COCO-O: A Benchmark for Object Detectors under Natural Distribution Shifts](https://mlsanity.com/paper/2307.12730/) (2023)
- [Learning to Aggregate Multi-Scale Context for Instance Segmentation in Remote Sensing Images](https://mlsanity.com/paper/2111.11057/) (2021)
- [Boosting R-CNN: Reweighting R-CNN Samples by RPN's Error for Underwater Object Detection](https://mlsanity.com/paper/2206.13728/) (2022)
- [FoodLogoDet-1500: A Dataset for Large-Scale Food Logo Detection via Multi-Scale Feature Decoupling Network](https://mlsanity.com/paper/2108.04644/) (2021)
- [Excavating RoI Attention for Underwater Object Detection](https://mlsanity.com/paper/2206.12128/) (2022)
- [A Mask Attention Interaction and Scale Enhancement Network for SAR Ship Instance Segmentation](https://mlsanity.com/paper/2207.03912/) (2022)
- [Recursively Refined R-CNN: Instance Segmentation with Self-RoI Rebalancing](https://mlsanity.com/paper/2104.01329/) (2021)
- [The MIS Check-Dam Dataset for Object Detection and Instance Segmentation Tasks](https://mlsanity.com/paper/2111.15613/) (2021)
- [DynamicVis: Dynamic Visual Perception for Efficient Remote Sensing Foundation Models](https://mlsanity.com/paper/2503.16426/) (2025)
- [Automated Optical Inspection of FAST's Reflector Surface using Drones and Computer Vision](https://mlsanity.com/paper/2212.09039/) (2022)
- [COUNTS: Benchmarking Object Detectors and Multimodal Large Language Models under Distribution Shifts](https://mlsanity.com/paper/2504.10158/) (2025)
- [LUAI Challenge 2021 on Learning to Understand Aerial Images](https://mlsanity.com/paper/2108.13246/) (2021)
- [GUNNEL: Guided Mixup Augmentation and Multi-Model Fusion for Aquatic Animal Segmentation](https://mlsanity.com/paper/2112.06193/) (2021)
- [Self-Balanced R-CNN for Instance Segmentation](https://mlsanity.com/paper/2404.16633/) (2024)
- [Renormalized Connection for Scale-preferred Object Detection in Satellite Imagery](https://mlsanity.com/paper/2409.05624/) (2024)
- [Learn Temporal Consistency For Robust Satellite Video Detector](https://mlsanity.com/paper/2606.15112/) (2026)
- [Multi-view feature High-order Fusion for Space Weak Object Detection and Segmentation](https://mlsanity.com/paper/2606.15118/) (2026)

## Abstract (arXiv)

Given the wide diffusion of deep neural network architectures for computer vision tasks, several new applications are nowadays more and more feasible. Among them, a particular attention has been recently given to instance segmentation, by exploiting the results achievable by two-stage networks (such as Mask R-CNN or Faster R-CNN), derived from R-CNN. In these complex architectures, a crucial role is played by the Region of Interest (RoI) extraction layer, devoted to extracting a coherent subset of features from a single Feature Pyramid Network (FPN) layer attached on top of a backbone. This paper is motivated by the need to overcome the limitations of existing RoI extractors which select only one (the best) layer from FPN. Our intuition is that all the layers of FPN retain useful information. Therefore, the proposed layer (called Generic RoI Extractor - GRoIE) introduces non-local building blocks and attention mechanisms to boost the performance. A comprehensive ablation study at component level is conducted to find the best set of algorithms and parameters for the GRoIE layer. Moreover, GRoIE can be integrated seamlessly with every two-stage architecture for both object detection and instance segmentation tasks. Therefore, the improvements brought about by the use of GRoIE in different state-of-the-art architectures are also evaluated. The proposed layer leads up to gain a 1.1% AP improvement on bounding box detection and 1.7% AP improvement on instance segmentation. The code is publicly available on GitHub repository at https://github.com/IMPLabUniPr/mmdetection/tree/groie_dev