Official Implementation of our Paper "Towards Bridged Vision and Language: Learning Cross-modal Knowledge Representation for Relation Extraction" in IEEE Transactions on Circuits and Systems for Video Technology.
We bridge semantic gaps between low-level visual objects and high-level textual relations with Cross-modal Knowledge graphs. We also provide some direct evidences for relation extraction. As a result, our method can generate the correct relations with corresponding reasons.
The overall framework of our proposed RECK model which introduces external knowledge to help predict textual relations. Three main modules are designed for multimodal semantic representation, cross-modal knowledge semantic representation, and final feature fusion and relation prediction. We construct the cross-modal knowledge graphs as a bridge to vision and language, thus the inconsistency problem of semantic levels can be addressed for precise relation identification.
Our model is based on the framework of OpenNRE. Please follow the instructions from their repository.
pip install opennre
You need to download two kinds of data to run the code.
You should specify the "root_path2" in run.py with the path of the raw images and objects.
The expected structures of Paths are:
|-- ckpt # save the checkpoint
| |-- best.pth.tar # we provide the model checkpoint for inference, you can also run the training code to get the same ckpt
|-- data
| |-- concept_data # the extracted knowledge paths (v2t, t2v & t2t graphs)
| | |-- val_0-5_v2t_entity.json
| | |-- val_0-5_t2v_entity.json
| | |-- val_0-5_t2t_entity.json
| | |-- test_0-5_v2t_entity.json
| | |-- test_0-5_t2v_entity.json
| | |-- test_0-5_t2t_entity.json
| | |-- train_0-5_v2t_entity.json
| | |-- train_0-5_t2v_entity.json
| | |-- train_0-5_t2t_entity.json
| |-- txt # text data
| | |-- ours_train.txt # input data
| | |-- ours_val.txt
| | |-- ours_test.txt
| | |-- mre_train_dict.pth # {imgname: [object-image]}
| | |-- ...
| |-- ours_rel2id.json # relation types
|-- opennre # main framework
| |-- encoder # main model
| | |-- bert_encoder.py # RECK model
| | |-- modeling_bert.py
| |-- framework # processing files
| | |-- data_loader.py # data processor, we read the concept paths here
| | |-- sentence_re.py # trainer
| | |-- utils.py
| |-- model # classifier
| | |-- softmax_nn.py # main classifier
| | |-- modeling_bert.py
| | |-- base_model.py # supporting the classifier, no modification required
| |-- tokenization # tokenizers, no modification required
| |-- pretrain.py # basic file
| |-- utils.py # basic file
|-- opennre.egg-info
|-- results # saving the results
| |-- test # results for test set
| |-- val # results for validation set
|-- run.py # main
The data path and GPU-related configuration are in the run.py. To train RECK, run the script:
python run.py --ckpt /path/to/your/checkpoint
The ckpt path can be simply set as "best" or "first".
To evaluate our model in the test set, simply run:
python run.py --ckpt best --only_test True
| Precision | Recall | F1 | |
|---|---|---|---|
| Reported Results | 88.77 | 88.91 | 88.84 |
| Implementation Results | 89.48 | 89.06 | 89.27 |
You can simply get the best results by runing the training code without changing any hyper-params.
If you find this repo helpful, please cite the following:
@article{feng2023towards,
title={Towards Bridged Vision and Language: Learning Cross-modal Knowledge Representation for Relation Extraction},
author={Feng, Junhao and Wang, Guohua and Zheng, Changmeng and Cai, Yi and Fu, Ze and Wang, Yaowei and Wei, Xiao-Yong and Li, Qing},
journal={IEEE Transactions on Circuits and Systems for Video Technology},
year={2023},
publisher={IEEE}
}
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