README.md

GRoIE

A novel Region of Interest Extraction Layer for Instance Segmentation

By Leonardo Rossi, Akbar Karimi and Andrea Prati from IMPLab.

We provide configs to reproduce the results in the paper for "A novel Region of Interest Extraction Layer for Instance Segmentation" on COCO object detection.

Introduction

[ALGORITHM]

This paper is motivated by the need to overcome to 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.

Results and models

The results on COCO 2017 minival (5k images) are shown in the below table. You can find here the trained models.

Application of GRoIE to different architectures

Backbone	Method	Lr schd	box AP	mask AP	Config	Download
R-50-FPN	Faster Original	1x	37.4		config	model | log
R-50-FPN	+ GRoIE	1x	38.3		config	model | log
R-50-FPN	Grid R-CNN	1x	39.1		config	model | log
R-50-FPN	+ GRoIE	1x			config
R-50-FPN	Mask R-CNN	1x	38.2	34.7	config	model | log
R-50-FPN	+ GRoIE	1x	39.0	36.0	config	model | log
R-50-FPN	GC-Net	1x	40.7	36.5	config	model | log
R-50-FPN	+ GRoIE	1x	41.0	37.8	config	model | log
R-101-FPN	GC-Net	1x	42.2	37.8	config	model | log
R-101-FPN	+ GRoIE	1x			config	model | log

Citation

If you use this work or benchmark in your research, please cite this project.

@misc{rossi2020novel,
    title={A novel Region of Interest Extraction Layer for Instance Segmentation},
    author={Leonardo Rossi and Akbar Karimi and Andrea Prati},
    year={2020},
    eprint={2004.13665},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}

Contact

The implementation of GROI is currently maintained by Leonardo Rossi.