Code for the cross-modal visual-linguistic retrieval method from "Fine-grained Visual Textual Alignment for Cross-modal Retrieval using Transformer Encoders", submitted to ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) [Pre-print PDF].
This work is an extension to our previous approach TERN accepted at ICPR 2020.
This repo is built on top of VSE++ and TERN.
Fine-grained Alignment for Precise Matching
Retrieval
- Clone the repo and move into it:
git clone https://github.com/mesnico/TERAN
cd TERAN
- Setup python environment using conda:
conda env create --file environment.yml
conda activate teran
export PYTHONPATH=.
- Download and extract the data folder, containing annotations, the splits by Karpathy et al. and ROUGEL - SPICE precomputed relevances for both COCO and Flickr30K datasets:
wget http://datino.isti.cnr.it/teran/data.tar
tar -xvf data.tar
- Download the bottom-up features for both COCO and Flickr30K. We use the code by Anderson et al. for extracting them.
The following command extracts them under
data/coco/anddata/f30k/. If you prefer another location, be sure to adjust the configuration file accordingly.
# for MS-COCO
wget http://datino.isti.cnr.it/teran/features_36_coco.tar
tar -xvf features_36_coco.tar -C data/coco
# for Flickr30k
wget http://datino.isti.cnr.it/teran/features_36_f30k.tar
tar -xvf features_36_f30k.tar -C data/f30k
Download and extract our pre-trained TERAN models:
wget http://datino.isti.cnr.it/teran/pretrained_models.tar
tar -xvf pretrained_models.tar
Then, issue the following commands for evaluating a given model on the 1k (5fold cross-validation) or 5k test sets.
python3 test.py pretrained_models/[model].pth --size 1k
python3 test.py pretrained_models/[model].pth --size 5k
Please note that if you changed some default paths (e.g. features are in another folder than data/coco/features_36), you will need to use the --config option and provide the corresponding yaml configuration file containing the right paths.
In order to train the model using a given TERAN configuration, issue the following command:
python3 train.py --config configs/[config].yaml --logger_name runs/teran
runs/teran is where the output files (tensorboard logs, checkpoints) will be stored during this training session.
WIP
If you found this code useful, please cite the following paper:
@article{messina2020finegrained,
title={Fine-grained Visual Textual Alignment for Cross-Modal Retrieval using Transformer Encoders},
author={Nicola Messina and Giuseppe Amato and Andrea Esuli and Fabrizio Falchi and Claudio Gennaro and Stéphane Marchand-Maillet},
journal={arXiv preprint arXiv:2008.05231},
year={2020},
}