DBL

PyTorch implementation for TIP2024 paper of “Deep Boosting Learning: A Brand-new Cooperative Approach for Image-Text Matching”.

It is built on top of the SGRAF, DML, DINO and Awesome_Matching.

If any problems, please contact me at [email protected]. ([email protected] is deprecated)

Introduction

The framework of DBL:

Requirements

Utilize pip install -r requirements.txt for the following dependencies.

• Python 3.7.11
• PyTorch 1.7.1
• NumPy 1.21.5
• Punkt Sentence Tokenizer:

import nltk
nltk.download()
> d punkt

Download data and vocab

We follow SCAN to obtain image features and vocabularies, which can be downloaded by using:

https://www.kaggle.com/datasets/kuanghueilee/scan-features

Another download link is available below：

https://drive.google.com/drive/u/0/folders/1os1Kr7HeTbh8FajBNegW8rjJf6GIhFqC

data
├── coco_precomp
│   ├── train_ids.txt
│   ├── train_caps.txt
│   └── ......
│
└── f30k_precomp
    ├── train_ids.txt
    ├── train_caps.txt
    └── ......

Pre-trained models and evaluation

Modify the model_path, split, fold5 in the test.py file. Note that fold5=True is only for evaluation on MSCOCO1K (5 folders average) while fold5=False for MSCOCO5K and Flickr30K. Pretrained models can be downloaded from Here with password [dhw4].

Then run python test.py in the terminal.

Training new models from scratch

Uncomment the required parts in the script.sh file.

Then run bash script.sh in the terminal:

Reference

If DBL is useful for your research, please cite the following paper:

@article{Diao2024DBL,
    author={Diao, Haiwen and Zhang, Ying and Gao, Shang and Ruan, Xiang and Lu, Huchuan},
    title={Deep Boosting Learning: {A} Brand-New Cooperative Approach for Image-Text Matching},
    journal={IEEE Transactions on Image Processing},
    year={2024},
    volume={33},
    pages={3341--3352}
}

License

Apache License 2.0.