Skip to content

[AAAI2021] The code of “Similarity Reasoning and Filtration for Image-Text Matching”

Notifications You must be signed in to change notification settings

Paranioar/SGRAF

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

45 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

SGRAF

PyTorch implementation for AAAI2021 paper of “Similarity Reasoning and Filtration for Image-Text Matching”.

It is built on top of the SCAN and Awesome_Matching.

We have released two versions of SGRAF: Branch main for python2.7; Branch python3.6 for python3.6.

If any problems, please contact me at r1228240468@gmail.com. (r1228240468@mail.dlut.edu.cn is deprecated)

Introduction

The framework of SGRAF:

The updated results (Better than the original paper)

Dataset Module Sentence retrieval Image retrieval
R@1R@5R@10 R@1R@5R@10
Flick30k SAF 75.692.796.9 56.582.088.4
SGR 76.693.796.6 56.180.987.0
SGRAF 78.494.697.5 58.283.089.1
MSCOCO1k SAF 78.095.998.5 62.289.595.4
SGR 77.396.098.6 62.189.695.3
SGRAF 79.296.598.6 63.590.295.8
MSCOCO5k SAF 55.583.891.8 40.169.780.4
SGR 57.383.290.6 40.569.680.3
SGRAF 58.884.892.1 41.670.981.5

Requirements

We recommended the following dependencies for Branch main.

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

Pre-trained models and evaluation

The pretrained models are only for Branch python3.6(python3.6), not for Branch main(python2.7).
Modify the model_path, data_path, vocab_path in the evaluation.py file. Then run evaluation.py:

python evaluation.py

Note that fold5=True is only for evaluation on mscoco1K (5 folders average) while fold5=False for mscoco5K and flickr30K. Pretrained models and Log files can be downloaded from Flickr30K_SGRAF and MSCOCO_SGRAF.

Training new models from scratch

Modify the data_path, vocab_path, model_name, logger_name in the opts.py file. Then run train.py:

For MSCOCO:

(For SGR) python train.py --data_name coco_precomp --num_epochs 20 --lr_update 10 --module_name SGR
(For SAF) python train.py --data_name coco_precomp --num_epochs 20 --lr_update 10 --module_name SAF

For Flickr30K:

(For SGR) python train.py --data_name f30k_precomp --num_epochs 40 --lr_update 30 --module_name SGR
(For SAF) python train.py --data_name f30k_precomp --num_epochs 30 --lr_update 20 --module_name SAF

Reference

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

  @inproceedings{Diao2021SGRAF,
     title={Similarity reasoning and filtration for image-text matching},
     author={Diao, Haiwen and Zhang, Ying and Ma, Lin and Lu, Huchuan},
     booktitle={Proceedings of the AAAI conference on artificial intelligence},
     volume={35},
     number={2},
     pages={1218--1226},
     year={2021}
  }

License

Apache License 2.0.

Releases

No releases published

Packages

No packages published

Languages