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--- |
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task_categories: |
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- image-segmentation |
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- object-detection |
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- image-to-text |
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--- |
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# gRefCOCO - Dataset for Generalized Referring Expression Segmentation, Comprehension, and Generation (GREx) |
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[](https://pytorch.org/) |
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[](https://www.python.org/downloads/) |
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[](https://paperswithcode.com/sota/generalized-referring-expression-segmentation?p=gres-generalized-referring-expression-1) |
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**[🏠[Project page]](https://henghuiding.github.io/GRES/)**   **[📄[Paper (GREx)]](https://huggingface.co/papers/2601.05244)**   **[📄[GRES Arxiv]](https://arxiv.org/abs/2306.00968)**   **[📄[GREC Arxiv]](https://arxiv.org/abs/2308.16182)** |
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This repository contains information and tools for the **gRefCOCO** dataset, proposed in the paper: |
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> [GREx: Generalized Referring Expression Segmentation, Comprehension, and Generation](https://huggingface.co/papers/2601.05244) |
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> Chang Liu, Henghui Ding, Xudong Jiang |
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gRefCOCO is the first large-scale GREx dataset that contains multi-target, no-target, and single-target expressions and their corresponding images with labeled targets. It is designed to be backward-compatible with classic Referring Expression (REx) tasks. |
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<div align="center"> |
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<img src="https://github.com/henghuiding/ReLA/blob/main/imgs/fig1.png?raw=true" width="100%" height="100%"/> |
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</div><br/> |
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## Usage |
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- Like RefCOCO, gRefCOCO also should be used together with images from the `train2014` of [MS COCO](https://cocodataset.org/#download). |
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- An example of dataloader [grefer.py](https://github.com/henghuiding/gRefCOCO/blob/main/grefer.py) is provided. |
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- 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. |
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## Task 1 - GREC: Generalized Referring Expression Comprehension |
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- The GREC evaluation metric code is [here](https://github.com/henghuiding/gRefCOCO/blob/main/mdetr/datasets/refexp.py). |
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- We provide code based on [MDETR](https://github.com/ashkamath/mdetr), its training and inference are as follows: |
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### Training (Finetuning) |
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1. Process grefcoco to coco format. |
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``` |
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python scripts/fine-tuning/grefexp_coco_format.py --data_path xxx --out_path mdetr_annotations/ --coco_path xxx |
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``` |
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2. Training and download `pretrained_resnet101_checkpoint.pth` from [MDETR](https://github.com/ashkamath/mdetr) |
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``` |
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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 |
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``` |
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### Inference |
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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) |
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2. For test results, pass --test and --test_type test or testA or testB according to the dataset. |
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``` |
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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 |
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``` |
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## Task 2 - GRES: Generalized Referring Expression Segmentation |
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Please refer to [ReLA](https://github.com/henghuiding/ReLA) for more details. |
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## Acknowledgement |
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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! |
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## BibTeX |
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Please consider to cite GRES/GREC/GREx if it helps your research. |
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```bibtex |
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@article{GREx, |
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title={{GREx}: Generalized Referring Expression Segmentation, Comprehension, and Generation}, |
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author={Liu, Chang and Ding, Henghui and Jiang, Xudong}, |
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journal={arXiv preprint arXiv:2601.05244}, |
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year={2026} |
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} |
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@inproceedings{GRES, |
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title={{GRES}: Generalized Referring Expression Segmentation}, |
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author={Liu, Chang and Ding, Henghui and Jiang, Xudong}, |
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booktitle={CVPR}, |
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year={2023} |
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} |
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@article{GREC, |
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title={{GREC}: Generalized Referring Expression Comprehension}, |
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author={He, Shuting and Ding, Henghui and Liu, Chang and Jiang, Xudong}, |
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journal={arXiv preprint arXiv:2308.16182}, |
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year={2023} |
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} |
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``` |
