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End of preview.

FG-CLIP: Fine-Grained Visual and Textual Alignment

FG-CLIP: Fine-Grained Visual and Textual Alignment
Chunyu Xie*, Bin Wang*, Fanjing Kong, Jincheng Li, Dawei Liang, Gengshen Zhang, Dawei Leng†, Yuhui Yin(*Equal Contribution, ✝Corresponding Author)
arXiv ICML GitHub

Model Framework

FG-CLIP’s training proceeds in two stages: the first stage leverages global-level caption-image pairs to achieve initial fine-grained alignment, while the second stage supplements these with additional region-level captions, including detailed region captions and positive/negative region descriptions to further refine the alignment.

Data Preparation

To run the training code for FG-CLIP, please follow the following step.

Step 1: Download the model

Download the FG-CLIP model from this link. 🤗Vit-L@336px or Download the OpenAI CLIP model from this link. 🤗Vit-L@336px

Step 2: Prepare FgGRN-12M (Fine-Grained visual Grounding+Recaption+Negative) Dataset

First, pull the dataset from the following link. 🤗FgGRN-12M,After downloading, unzip all compressed files, you will obtain the following file structure:

FgGRN-12M
├── url2key_jsons
|   ├── url2key_coyo_image_0.json
|   ├── ...
│   ├── url2key_coyo_image_20.json
├── jsonfiles
|   ├── 2024-12-06_18-32-53_results_10_218_126_44_1025.json
│   ├── 2024-12-06_18-33-17_results_llama70b-shcdt-h100-4gpus-no-2.json
│   ├──...
├── coyo_image_0
|   ├── 00000.parquet
│   ├── 00001.parquet
│   ├── ...
│   ├── 00099.parquet
├── coyo_image_1
|   ├── 00000.parquet
│   ├── 00001.parquet
│   ├── ...
│   ├── 00099.parquet
├── ...
├── coyo_image_20
|   ├── 00000.parquet
│   ├── 00001.parquet
│   ├── ...
│   ├── 00050.parquet
├── ...

Subsequently, you need to install the img2dataset package. You can do this by running the following command:

pip install img2dataset

Set the file_in parameter in the script (data/get_data.sh) according to the download path of the data, and also set the directory where you expect to save the files (pre_dir, dir_save). Subsequently, execute the following commands.

bash data/get_data.sh

Due to the randomness in downloading, the image names corresponding to the URLs do not match the names of the images we are using. Therefore, a conversion is needed. This step requires using the url2key_jsons/*.json file included in the FgGRN-12M dataset. Also, you can use the files in url2key_jsons/*.json to check the download links of all the images we used.

python -m data.convert_image_name \
    --url2key_json FgGRN-12M/url2key_jsons \
    --down_file_root data/down-grit-12m/ \
    --num_parent_folders 21 \
    --num_subfolders_per_parent 100 \
    --resave_file_root data/grit-12m/ \

rm -r data/down-grit-12m/
FG-CLIP
├── ...
├── FgGRN-12M
|   ├── jsonfiles
|   |   ├── 2024-12-06_18-32-53_results_10_218_126_44_1025.json
|   |   ├── 2024-12-06_18-33-17_results_llama70b-shcdt-h100-4gpus-no-2.json
|   |   ├──...
|   ├── ...
├── data
|   ├── grit-12m
|   |   ├── coyo_image_0
|   |   |   ├──00000
|   |   |   ├──00001
|   |   |   ├──...
|   |   |   ├──00099
|   |   ├── coyo_image_1
|   |   |   ├──00000
|   |   |   ├──00001
|   |   |   ├──...
|   |   |   ├──00099
|   |   ├── ...
|   |   ├── coyo_image_20
|   |   |   ├──00000
|   |   |   ├──00001
|   |   |   ├──...
|   |   |   ├──00050
├── ...

Citation

If you find FgGRN-12M useful for your research and applications, please cite using this BibTeX:

@article{xie2025fgclip,
      title={FG-CLIP: Fine-Grained Visual and Textual Alignment}, 
      author={Chunyu Xie and Bin Wang and Fanjing Kong and Jincheng Li and Dawei Liang and Gengshen Zhang and Dawei Leng and Yuhui Yin},
      year={2025},
      eprint={2505.05071},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2505.05071}, 
}
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