Awesome

Bi-directional Image and Text Generation

UMT-BITG (image & text generator)

Unifying Multimodal Transformer for Bi-directional Image and Text Generation,
Yupan Huang, Bei Liu, Yutong Lu, in ACM MM 2021 (Industrial Track).

UMT-DBITG (diverse image & text generator)

A Picture is Worth a Thousand Words: A Unified System for Diverse Captions and Rich Images Generation,
Yupan Huang, Bei Liu, Jianlong Fu, Yutong Lu, in ACM MM 2021 (Video and Demo Track).

Poster or slides are available in the assets folder by visiting OneDrive.

Data & Pre-trained Models

Download preprocessed data and our pre-trained models by visiting OneDrive. We suggest following our data structures, which is consistent with the paths in config.py. You may need to modify the root_path in config.py. In addition, please following the instructions to prepare some other data:

• Download grid features in path data/grid_features provided by X-LXMERT or follow feature extraction to extract these features.

  wget https://ai2-vision-x-lxmert.s3-us-west-2.amazonaws.com/butd_features/COCO/maskrcnn_train_grid8.h5 -P data/grid_features
  wget https://ai2-vision-x-lxmert.s3-us-west-2.amazonaws.com/butd_features/COCO/maskrcnn_valid_grid8.h5 -P data/grid_features
  wget https://ai2-vision-x-lxmert.s3-us-west-2.amazonaws.com/butd_features/COCO/maskrcnn_test_grid8.h5 -P data/grid_features
  

• For text-to-image evaluation on MSCOCO dataset, we need the real images to calculate the FID metric. For UMT-DBITG, we use MSCOCO karpathy split, which has been included in the OneDrive folder (images/imgs_karpathy). For UMT-BITG, please download MSCOCO validation set in path images/coco_val2014.

Citation

If you like our paper or code, please generously cite us:

@inproceedings{huang2021unifying,
  author    = {Yupan Huang and Bei Liu and Yutong Lu},
  title     = {Unifying Multimodal Transformer for Bi-directional Image and Text Generation},
  booktitle = {Proceedings of the 29th ACM International Conference on Multimedia},
  year      = {2021}
}

@inproceedings{huang2021diverse,
  author    = {Yupan Huang and Bei Liu and Jianlong Fu and Yutong Lu},
  title     = {A Picture is Worth a Thousand Words: A Unified System for Diverse Captions and Rich Images Generation},
  booktitle = {Proceedings of the 29th ACM International Conference on Multimedia},
  year      = {2021}
}

Acknowledgement

Our code is mainly based on LaBERT and X-LXMERT. Our text-to-image generation evaluation code is mainly based on CLIP, pytorch-fid and inception_score. We sincerely thank them for their contributions!

Feel free to open issues or email to me for help to use this code. Any feedback is welcome!