Implementation of the paper ''Implicit Feature Refinement for Instance Segmentation''.

Related tags

Deep Learning IFR
Overview

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.

pipeline

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

  1. 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.

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

  3. 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

Model AP AP50 AP75 APs APm APl Link
mask_rcnn.ifr.res50.fpn.coco.multiscale.1x 36.3 56.8 39.2 17.3 39.0 52.2 download
mask_rcnn.res50.fpn.coco.multiscale.weight_sharing.1x 35.9 56.7 38.5 17.1 38.5 51.8 download
cascade_rcnn.ifr.res50.fpn.coco.800size.1x 36.9 57.1 39.8 17.4 39.3 54.6 download

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.

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Comments
  • Explanation of parameter self.pretrain_steps

    Explanation of parameter self.pretrain_steps

    Thank you or your great work. I have two confusions:

    1. I don't understand the meaning of self.pretrain_steps. Could you please explain?
    2. Did you use the MDEQ method? (eg, apply the Implicit Feature Refinement to multi-resolution feature), but I feel as if DEQ is applied to the feature Pi (eg, P4) for this paper. question
    opened by Darcy103 2
Owner
Lufan Ma
Lufan Ma
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