Awesome
(MyGO) Tokenization, Fusion, and Augmentation: Towards Fine-grained Multi-modal Entity Representation
Overview
🎆 News
2024-12
🎉🎉🎉 Our paper is accepted by AAAI 2025. The title is changed to Tokenization, Fusion, and Augmentation: Towards Fine-grained Multi-modal Entity Representation.2024-04
Our paper and code are released on ArXiV and Github.2024-02
We preprint our Survey Knowledge Graphs Meet Multi-Modal Learning: A Comprehensive Survey [Repo
].
Dependencies
pip install -r requirement.txt
Details
- Python==3.9
- numpy==1.24.2
- scikit_learn==1.2.2
- torch==2.0.0
- tqdm==4.64.1
- transformers==4.28.0
Data Preparation
You should first get the textual token embedding by running save_token_embeddings.py
with transformers library (BERT, RoBERTa, LlaMA). You can first try MyGO on the pre-processed datasets DB15K, MKG-W, and MKG-Y. The large token files in tokens/
should be unzipped before using in the training process. We provide VQGAN / BEiT tokens for visual modality and BERT / RoBERTa / LlaMA tokens for textual modality.
Train and Evaluation
You can refer to the training scripts in run.sh
to reproduce our experiment results. Here is an example for DB15K dataset.
CUDA_VISIBLE_DEVICES=0 nohup python train_mygo_fgc.py --data DB15K --num_epoch 1500 --hidden_dim 1024 --lr 1e-3 --dim 256 --max_vis_token 8 --max_txt_token 4 --num_head 2 --emb_dropout 0.6 --vis_dropout 0.3 --txt_dropout 0.1 --num_layer_dec 1 --mu 0.01 > log.txt &
More training scripts can be found in run.sh
.
🤝 Citation
@misc{zhang2024mygo,
title={MyGO: Discrete Modality Information as Fine-Grained Tokens for Multi-modal Knowledge Graph Completion},
author={Yichi Zhang and Zhuo Chen and Lingbing Guo and Yajing Xu and Binbin Hu and Ziqi Liu and Huajun Chen and Wen Zhang},
year={2024},
eprint={2404.09468},
archivePrefix={arXiv},
primaryClass={cs.AI}
}