ben2002chou/fusemix

Data-Efficient Multimodal Fusion on a Single GPU

Data-Efficient Multimodal Fusion on a Single GPU

Noël Vouitsis*, Zhaoyan Liu*, Satya Krishna Gorti*, Valentin Villecroze, Jesse C. Cresswell, Guangwei Yu, Gabriel Loaiza-Ganem, Maksims Volkovs

Introduction

This repository contains the official implementation of our CVPR 2024 Highlight paper Data-Efficient Multimodal Fusion on a Single GPU. We release code for the image-text setting, including code for dataset downloading, feature extraction, fusion training and evaluation. We note that our code is based on the LAVIS library.

Installation

1. (Optional) Creating conda environment

conda create -n fusemix python=3.8
conda activate fusemix

2. Build from source

git clone https://github.com/layer6ai-labs/fusemix
cd fusemix
pip install -e .

Getting Started

Model Zoo

Model zoo summarizes supported models, to view:

from lavis.models import model_zoo
print(model_zoo)
# ======================================================================
# Architectures                            Types
# ======================================================================
# dinov2_feature_extractor                 vits14, vitb14, vitl14, vitg14
# bge_feature_extractor                    large
# cohere_feature_extractor                 v3
# mlp_contrastive_fusion                   base

Dataset Zoo

Dataset zoo summarizes supported datasets, to view:

from lavis.datasets.builders import dataset_zoo
dataset_names = dataset_zoo.get_names()
print(dataset_names)

Dataset Downloading

Please refer to lavis/datasets/download_scripts for scripts to download the required datasets.

Feature Extraction

bash run_scripts/feature_extract/feat_extract_bge_large_coco_cap.sh

FuseMix Training

bash run_scripts/fusion/mlp_contrastive_fusion_pretrain_dinov2_vitg14_bge_large_coco_vg_sbu_cap_cc3m.sh

Evaluation

bash run_scripts/fusion/mlp_contrastive_fusion_retrieval_dinov2_vitg14_bge_large_coco.sh

Citation

If you find this work useful in your research, please cite the following paper:

@inproceedings{vouitsis2024dataefficient,
      title={Data-Efficient Multimodal Fusion on a Single GPU}, 
      author={No{\"e}l Vouitsis and Zhaoyan Liu and Satya Krishna Gorti and Valentin Villecroze and Jesse C. Cresswell and Guangwei Yu and Gabriel Loaiza-Ganem and Maksims Volkovs},
      booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
      year={2024},
}