This package contains the accompanying code for the following paper:

Tu, Yunbin, et al. "I²Transformer: Intra- and Inter-relation Embedding Transformer for TV Show Captioning.", which has appeared as regular paper in IEEE TIP。

Download tvc_feature_release.tar.gz (23GB). After downloading the file, extract it to the data directory.

tar -xf path/to/tvc_feature_release.tar.gz -C data

You should be able to see video_feature under data/tvc_feature_release directory. It contains video features (ResNet, I3D, ResNet+I3D). Plase note that this code only used the features of ResNet+I3D.

• Ubuntu 16.04
• Python 2.7
• PyTorch 1.1.0
• nltk
• easydict
• tqdm
• h5py
• tensorboardX
• An RTX 2080Ti

source setup.sh

Note that you need to do this each time you start a new session.

git clone https://github.com/tylin/coco-caption.git

You could skip this step because this file has been provided.

bash baselines/multimodal_transformer/scripts/build_vocab.sh

Running this command will build vocabulary cache/tvc_word2idx.json from TVC train set.

bash baselines/multimodal_transformer/scripts/train.sh video_sub resnet_i3d

This code will load all the data (~30GB) into RAM to speed up training, use --no_core_driver to disable this behavior.

Training using the above config will stop at around epoch 22, around 7 hours with a single 2080Ti GPU. You should get ~47.2 CIDEr and ~11.4 BLEU@4 scores on val set. The resulting model and config will be saved at a dir: baselines/multimodal_transformer/results/video_sub-res-*

After training, you can inference using the saved model on val or test_public set:

bash baselines/multimodal_transformer/scripts/translate.sh MODEL_DIR_NAME SPLIT_NAME

MODEL_DIR_NAME is the name of the dir containing the saved model, e.g., video_sub-res-*. SPLIT_NAME could be val or test_public.

The generated captions and evaluation scores on the val and test_public set are in the dir: 'our_results'

If you find this helps your research, please consider citing:

@article{tu2022i2transformer,
  title={I2Transformer: Intra-and Inter-relation Embedding Transformer for TV Show Captioning},
  author={Tu, Yunbin and Li, Liang and Su, Li and Gao, Shengxiang and Yan, Chenggang and Zha, Zheng-Jun and Yu, Zhengtao and Huang, Qingming},
  journal={IEEE Transactions on Image Processing},
  year={2022},
  publisher={IEEE}
}

My email is [email protected]

Any discussions and suggestions are welcome!

This work and code are inspired by TVCaption. Thanks for their solid work!