(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}
}