Image Captioning and Text-to-Image Synthesis with textual data augmentation

This code run well under python2.7 and TensorFlow 0.11, if you use higher version of TensorFlow you may need to update the tensorlayer folder from TensorLayer Lib.

Usage

1. Prepare MSCOCO data and Inception model

• Before you run the scripts, you need to follow Google's setup guide, and setup the model, ckpt and data directories in *.py.
  • Creat a data folder.
  • Download and Preprocessing MSCOCO Data click here.
  • Download the Inception_V3 CKPT click here.

2. Train image captioning model

• Train your image captioning model on MSCOCO by following my other repo.

3. Setup your paths

• in train_im2txt2im_coco_64.py
• config your image directory here images_train_dir = '/home/.../mscoco/raw-data/train2014/'
• config the vocabulary and model of you image captioning module DIR = "/home/..."
• directory containing model checkpoints. CHECKPOINT_DIR = DIR + "/model/train"
• vocabulary file generated by the preprocessing script. VOCAB_FILE = DIR + "/data/mscoco/word_counts.txt"

4. Train text-to-image synthesis with image captioning

• model_im2txt.py model for image captioning
• train_im2txt2im_coco_64.py script for training I2T2I
• utils.py script for utility functions

Results

1. Here are some results on MSCOCO

2. Transfer learning on MHP dataset

Citation

• If you find it is useful, please cite:

@article{hao2017im2txt2im,
  title={I2T2I: LEARNING TEXT TO IMAGE SYNTHESIS WITH TEXTUAL DATA AUGMENTATION},
  author={Hao Dong, Jingqing Zhang, Douglas McIlwraith, Yike Guo},
  journal={ICIP},
  year={2017}
}