chihhuiho / yoro

General

This is the official code for YORO - Lightweight End to End Visual Grounding, accepted by European Conference On Computer Vision (ECCV) Workshop on International Challenge on Compositional and Multimodal Perception, Tel-Aviv, Israel, 2022

Evironment

Use environment/environment.yml or environment/environment_cuda102.yml depending on cuda version for creating the environment.

conda env create -f environment/environment.yml 
conda activate yoro
python -m spacy download en_core_web_sm

Datasets

1. Comment out the dataset in download.sh that is not needed. It takes few hours to download all datasets
2. Download the dataset to the "./dataset/raw" folder by

sh download.sh

Preprocess Dataset

Converting the raw data to arrow format.

1. Comment out the dataset in preprocess_dataset.py that is not needed

python preprocess_dataset.py

2. The preprocessed dataset will be stored in "./dataset/arrow"

Download Pretrained weight

Vilt weight for pretraining

cd pretrained_weight
sh download_weight.sh

Yoro weight for various VG tasks

1. Download result.zip from google drive google drive
2. unzip the result.zip

Evaluation

For each eval.sh file in the script/DATASET, change the flag "debug" to False to run full evaluation. Below, we will describe how to run the eval.sh for different datasets.

Pretraining tasks

sh script/pretrain/eval.sh

Downstream tasks

RefCoco Dataset

sh script/RefCoco/eval.sh

RefCoco+ Dataset

sh script/RefCocoP/eval.sh

RefCocog Dataset

sh script/RefCocog/eval.sh

CopsRef Dataset

sh script/copsref/eval.sh

ReferItGame/RefClef Dataset

sh script/ReferItGame/eval.sh

Training

For all run.sh file, please change the "debug" flag to True to run the full training.

Pretraining tasks

For Modulated detection pretraining, we start from a mlm-itm pretrained model, such as the vilt pretraining checkpoint. For example, the below script is for training with 5 det tokens for 40 epochs on 1 gpu. Please refer to the comment in the script for more details.

sh script/pretrain/run.sh 5 40 1

Downstream tasks

RefCoco Dataset

For RefCoco dataset, we load the pretraining checkpoints as initial weight. For example, the below script is for training with 5 det tokens for 10 epochs on 1 gpu. Please refer to the comment in the script for more details.

sh script/RefCoco/run.sh 5 10 1

RefCoco+ Dataset

For RefCoco+ dataset, we load the pretraining checkpoints as initial weight. For example, the below script is for training with 5 det tokens for 10 epochs on 1 gpu. Please refer to the comment in the script for more details.

sh script/RefCocoP/run.sh 5 10 1

RefCocog Dataset

For RefCocog dataset, we load the pretraining checkpoints as initial weight. For example, the below script is for training with 5 det tokens for 10 epochs on 1 gpu. Please refer to the comment in the script for more details.

sh script/RefCocog/run.sh 5 10 1

CopsRef Dataset

For copsref dataset, we load the pretraining checkpoints as initial weight. For example, the below script is for training with 5 det tokens for 40 epochs on 1 gpu. Please refer to the comment in the script for more details.

sh script/copsref/run.sh 5 40 1

ReferItGame/RefClef Dataset

For ReferItGame/RefClef dataset, we load the pretraining checkpoints as initial weight. For example, the below script is for training with 5 det tokens for 40 epochs on 1 gpu. Please refer to the comment in the script for more details.

sh script/ReferItGame/run.sh 5 40 1

Citation

If you find this method useful in your research, please cite this article:

@inproceedings{ho2022yoro,
  title={YORO-Lightweight End to End Visual Grounding},
  author={Ho, Chih-Hui and Appalaraju, Srikar and Jasani, Bhavan and Manmatha, R and Vasconcelos, Nuno},
  booktitle={ECCV 2022 Workshop on International Challenge on Compositional and Multimodal Perception},
  year={2022}
}

Acknowledgement

Please email to Chih-Hui (John) Ho ([e-mail]) if further issues are encountered. We heavily used the code from

1. https://github.com/dandelin/ViLT
2. https://github.com/ashkamath/mdetr