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SimREC

SimREC is a simple and lightweight codebase for the research of referring expression comprehension and segmentation, supporting large-scale pre-training and multi-task learning. SimREC includes the official implementations of our paper "Multi-task Collaborative Network for Joint Referring Expression Comprehension and Segmentation" and "What Goes beyond Multi-modal Fusion in One-stage Referring Expression Comprehension: An Empirical Study" . More models will be updated.

Updates

• (2022/4/20) A novel point-based prediction paradigm SeqTR is released in here, which will be updated in this repository.
• (2022/4/20) Implement MCN for SimREC project.
• (2022/4/19) Release our SimREC project.

Installation

• Clone this repo

git clone https://github.com/luogen1996/SimREC.git
cd SimREC

• Create a conda virtual environment and activate it

conda create -n simrec python=3.7 -y
conda activate simrec

• Install Pytorch following the official installation instructions
• Install mmcv following the installation guide
• Install Spacy and initialize the GloVe and install other requirements as follows:

pip install -r requirements.txt
wget https://github.com/explosion/spacy-models/releases/download/en_vectors_web_lg-2.1.0/en_vectors_web_lg-2.1.0.tar.gz -O en_vectors_web_lg-2.1.0.tar.gz
pip install en_vectors_web_lg-2.1.0.tar.gz

Data preparation

•  Follow the instructions of DATA_PRE_README.md to generate training data and testing data.
•  Download the pretrained weights of backbone (vgg, darknet, cspdarknet, DResNet, etc.). Expect for DResNet, all pretrained backbones are trained on COCO 2014 train+val set while removing the images appeared in the val+test sets of RefCOCO, RefCOCO+ and RefCOCOg (nearly 6500 images). Please follow the instructions of DATA_PRE_README.md to download them.

Training and Evaluation

1. Config preparation. Prepare your own configs in configs, you don't need to rewrite all the contents in config every time.You can import the config as a python file to use the default configs in configs/common as follows:

# your own config.py
from simrec.config import LazyCall
from .common.dataset import dataset
from .common.train import train
from .common.optim import optim
from .common.models.simrec import model

# modify the config depend your own need
dataset.ann_path["refcoco"] = "path/to/refcoco_data"
train.output_dir = "./path/to/output_dir"
...

2. Train the model. Run bash/train.sh under the tools to start training, for example, training simrec model on 4 GPUs:

bash tools/train.sh config/simrec_refcoco_scratch.py 4

The training logs, tensorboard logs, config.yaml and model checkpoints will be automatically saved under cfg.train.output_dir.

3. Resume training. We support two resume training mode. You can resume from a specific checkpoint or resume from the latest checkpoint:

• Auto resume from last_checkpoint.pth:

# config.py
from .common.train import train
train.auto_resume.enabled = True

Setting train.auto_resume.enabled=True, which will automatically resume from last_checkpoint.pth saved in cfg.train.output_dir.

• Resume from a specific checkpoint

# config.py
from .common.train import train

# disable auto resume first
train.auto_resume.enabled = False

# modify the resume path
train.resume_path = "path/to/specific/checkpoint.pth"

Setting train.resume_path to the specific checkpoint.pth you want to resume from.

4. Test the model. Run bash/eval.sh under tools to evaluate the saved weight.

bash tools/eval.sh config/simrec_refcoco_scratch.py 4 /path/to/checkpoint

Model Zoo

SimREC supports all benchmarks of REC and RES, and can easily achieve very competitive performance. More results are available in Model Zoo.

License

This project is released under the Apache 2.0 license.

Citation

If this repository is helpful for your research, or you want to refer the provided results in your paper, consider cite:

@article{luo2022what,
  title={What Goes beyond Multi-modal Fusion in One-stage Referring Expression Comprehension: An Empirical Study},
  author={Luo, Gen and Zhou, Yiyi and Sun, Jiamu and Huang, Shubin and Sun, Xiaoshuai and Ye, Qixiang and Wu, Yongjian and Ji, Rongrong},
  journal={arXiv preprint arXiv:2204.07913},
  year={2022}
}

@article{zhu2022seqtr,
  title={SeqTR: A Simple yet Universal Network for Visual Grounding},
  author={Zhu, Chaoyang and Zhou, Yiyi and Shen, Yunhang and Luo, Gen and Pan, Xingjia and Lin, Mingbao and Chen, Chao and Cao, Liujuan and Sun, Xiaoshuai and Ji, Rongrong},
  journal={arXiv preprint arXiv:2203.16265},
  year={2022}
}

@InProceedings{Luo_2020_CVPR,
author = {Luo, Gen and Zhou, Yiyi and Sun, Xiaoshuai and Cao, Liujuan and Wu, Chenglin and Deng, Cheng and Ji, Rongrong},
title = {Multi-Task Collaborative Network for Joint Referring Expression Comprehension and Segmentation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2020}
}

@inproceedings{luo2020cascade,
  title={Cascade grouped attention network for referring expression segmentation},
  author={Luo, Gen and Zhou, Yiyi and Ji, Rongrong and Sun, Xiaoshuai and Su, Jinsong and Lin, Chia-Wen and Tian, Qi},
  booktitle={Proceedings of the 28th ACM International Conference on Multimedia},
  pages={1274--1282},
  year={2020}
}

@article{zhou2021real,
  title={A real-time global inference network for one-stage referring expression comprehension},
  author={Zhou, Yiyi and Ji, Rongrong and Luo, Gen and Sun, Xiaoshuai and Su, Jinsong and Ding, Xinghao and Lin, Chia-Wen and Tian, Qi},
  journal={IEEE Transactions on Neural Networks and Learning Systems},
  year={2021},
  publisher={IEEE}
}

@misc{luo2022simrec,
  author =       {Gen Luo and Tianhe Ren},
  title =        {SimREC},
  howpublished = {\url{https://github.com/luogen1996/SimREC}},
  year =         {2022}
}

Acknowledgement

Thanks a lot for the nicely organized code from the following repos

• OpenVQA.
• Swin-Transformer
• Detectron2