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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

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.

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 42.6 38.7 config model | log

Citation

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

@inproceedings{rossi2021novel,
  title={A novel region of interest extraction layer for instance segmentation},
  author={Rossi, Leonardo and Karimi, Akbar and Prati, Andrea},
  booktitle={2020 25th International Conference on Pattern Recognition (ICPR)},
  pages={2203--2209},
  year={2021},
  organization={IEEE}
}

Contact

The implementation of GRoIE is currently maintained by Leonardo Rossi.