# gRefCOCO - Dataset for [CVPR2023 Highlight] GRES: Generalized Referring Expression Segmentation
[](https://pytorch.org/)
[](https://www.python.org/downloads/)
[](https://paperswithcode.com/sota/generalized-referring-expression-segmentation?p=gres-generalized-referring-expression-1)
**[🏠[Project page]](https://henghuiding.github.io/GRES/)** **[📄[GRES Arxiv]](https://arxiv.org/abs/2306.00968)** **[📄[GREC Arxiv]](https://arxiv.org/abs/2308.16182)**
This repository contains information and tools for the [gRefCOCO](https://henghuiding.github.io/GRES/) dataset, proposed by the **CVPR2023 Highlight** paper:
> [GRES: Generalized Referring Expression Segmentation](https://arxiv.org/abs/2306.00968)
> Chang Liu, Henghui Ding, Xudong Jiang
> CVPR 2023 Highlight, Acceptance Rate 2.5%
## Usage
- Like RefCOCO, gRefCOCO also should be used together with images from the `train2014` of [MS COCO](https://cocodataset.org/#download).
- An example of dataloader [grefer.py](https://github.com/henghuiding/gRefCOCO/blob/main/grefer.py) is provided.
- We will update this repository with full API package and documentation soon. Please follow the usage in the [baseline code](https://github.com/henghuiding/ReLA) for now.
## Task 1 - GREC: Generalized Referring Expression Comprehension
- The GREC evaluation metric code is [here](https://github.com/henghuiding/gRefCOCO/blob/main/mdetr/datasets/refexp.py).
- We provide code based on [MDETR](https://github.com/ashkamath/mdetr), its training and inference are as follows:
### Training (Finetuning)
1. Process grefcoco to coco format.
```
python scripts/fine-tuning/grefexp_coco_format.py --data_path xxx --out_path mdetr_annotations/ --coco_path xxx
```
2. Training and download `pretrained_resnet101_checkpoint.pth` from [MDETR](https://github.com/ashkamath/mdetr)
```
python -m torch.distributed.launch --nproc_per_node=2 --use_env main.py --dataset_config configs/grefcoco.json --batch_size 4 --load pretrained_resnet101_checkpoint.pth --ema --text_encoder_lr 1e-5 --lr 5e-5 --output-dir grefcoco
```
### Inference
1. Obtain `checkpoint.pth` after training or download trained model [ here ☁️ Google Drive](https://drive.google.com/file/d/14OrM3n_Oap7xCT6nxj9QEnkJOUMpBGjB/view?usp=drive_link)
2. For test results, pass --test and --test_type test or testA or testB according to the dataset.
```
python -m torch.distributed.launch --nproc_per_node=2 --use_env main.py --dataset_config configs/grefcoco.json --batch_size 4 --resume grefcoco/checkpoint.pth --ema --eval
```
## Task 2 - GRES: Generalized Referring Expression Segmentation
Please refer to [ReLA](https://github.com/henghuiding/ReLA) for more details.
## Acknowledgement
Our project is built upon [refer](https://github.com/lichengunc/refer) and [cocoapi](https://github.com/cocodataset/cocoapi). Many thanks to the authors for their great works!
## BibTeX
Please consider to cite GRES/GREC if it helps your research.
```bibtex
@inproceedings{GRES,
title={{GRES}: Generalized Referring Expression Segmentation},
author={Liu, Chang and Ding, Henghui and Jiang, Xudong},
booktitle={CVPR},
year={2023}
}
@article{GREC,
title={{GREC}: Generalized Referring Expression Comprehension},
author={He, Shuting and Ding, Henghui and Liu, Chang and Jiang, Xudong},
journal={arXiv preprint arXiv:2308.16182},
year={2023}
}
```
We also recommend other highly related works:
```bibtex
@article{VLT,
title={{VLT}: Vision-language transformer and query generation for referring segmentation},
author={Ding, Henghui and Liu, Chang and Wang, Suchen and Jiang, Xudong},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
year={2023},
volume={45},
number={6},
publisher={IEEE}
}
@inproceedings{MeViS,
title={{MeViS}: A Large-scale Benchmark for Video Segmentation with Motion Expressions},
author={Ding, Henghui and Liu, Chang and He, Shuting and Jiang, Xudong and Loy, Chen Change},
booktitle={ICCV},
year={2023}
}
```
