This is the repository of the A Deep Local and Global Scene Graph Matching for Image-Text Retrieval paper which is accepted in SOMET2021. This research is inspired by the SGM paper and can be considered as an major improvement of it. The comparison can be fully described in our paper.
Our code is mostly based on the SGM original code.
For those who are interested in MSCOCO Data, I have uploaded the preprocess data and also the original scene graph extracted from images and their captions. You can find them here. The original data is given by the authors of the SGM model.
The presentation slide in the SOMET2021 is here.
Please install packages in the requirements.txt. The project is implemented with python 3.7.9
Our data (Flickr30k) is original given by the SGM paper. We only performed same basic cleaning process to remove duplicated data and lowering text. The preprocessed data can be found in the Data folder.
The model also need the visual features which are the embedded vector of objects in images. In this research, we used EfficientNet-b5 to extract the features. You can extract by running extract_visual_features.py script. We also uploaded our prepared features (here). You can download it and place in the Data folder.
You can run the main_train.py script to perform either training or evaluating the model. Our pretrained model can be found here. Please download it and place in Report folder.
For any issue or comment, you can directly email me at manh.nguyen5@mail.dcu.ie
For citation, you can add the bibtex as following:
@article{nguyen2021deep,
title={A Deep Local and Global Scene-Graph Matching for Image-Text Retrieval},
author={Nguyen, Manh-Duy and Nguyen, Binh T and Gurrin, Cathal},
journal={arXiv preprint arXiv:2106.02400},
year={2021}
}