Low level features like edges and textures play an important role in accurately localizing instances in neural networks. In this paper, we propose an architecture which improves feature pyramid networks commonly used instance segmentation networks by incorporating low level features in all layers of the pyramid in an optimal and efficient way. Specifically, we introduce a new layer which learns new correlations from feature maps of multiple feature pyramid levels holistically and enhances the semantic information of the feature pyramid to improve accuracy. Our architecture is simple to implement in instance segmentation or object detection frameworks to boost accuracy. Using this method in Mask RCNN, our model achieves consistent improvement in precision on COCO Dataset with the computational overhead compared to the original feature pyramid network.
@article{arxiv.1904.00768,
title = {Concatenated Feature Pyramid Network for Instance Segmentation},
author = {Yongqing Sun and Pranav Shenoy K P and Jun Shimamura and Atsushi Sagata},
journal= {arXiv preprint arXiv:1904.00768},
year = {2019}
}