Implicit Feature Refinement for Instance Segmentation

This repository is an official implementation of the ACM Multimedia 2021 paper Implicit Feature Refinement for Instance Segmentation.

Introduction

TL; DR. Implicit feature refinement (IFR) enjoys several advantages: 1) simulates an infinite-depth refinement network while only requiring parameters of single residual block; 2) produces high-level equilibrium instance features of global receptive field; 3) serves as a general plug-and-play module easily extended to most object recognition frameworks.

Get Started

1. Install cvpods following the instructions

# Install cvpods
git clone https://github.com/Megvii-BaseDetection/cvpods.git
cd cvpods 
## build cvpods (requires GPU)
python3 setup.py build develop
## preprare data path
mkdir datasets
ln -s /path/to/your/coco/dataset datasets/coco

2. To save the training and testing time, the explicit form of our IFR, annotated with "weight_sharing", is provided on mask_rcnn to achieve competitive performance.

3. For fast evaluation, please download trained model from here.

4. Run the project

git clone https://github.com/lufanma/IFR.git

# for example(e.g. mask_rcnn.ifr)
cd IFR/mask_rcnn.ifr.res50.fpn.coco.multiscale.1x/

# train
sh pods_train.sh

# test
sh pods_test.sh
# test with provided weights
sh pods_test.sh \
    MODEL.WEIGHTS /path/to/your/save_dir/ckpt.pth # optional
    OUTPUT_DIR /path/to/your/save_dir # optional

Results

Citing IFR

If you find IFR useful to your research, please consider citing:

@inproceedings{ma2021implicit,
  title={Implicit Feature Refinement for Instance Segmentation},
  author={Ma, Lufan and Wang, Tiancai and Dong, Bin and Yan, Jiangpeng and Li, Xiu and Zhang, Xiangyu},
  booktitle={Proceedings of the 29th ACM International Conference on Multimedia},
  pages={3088--3096},
  year={2021}
}

Given thanks to the open source of DEQ and MDEQ, our IFR is developed based on them.