This contains the source code for Show, Edit and Tell: A Framework for Editing Image Captions, to appear at CVPR 2020

• Python 3.6 or 3.7
• PyTorch 1.2

For evaluation, you also need:

• Java 1.8.0
• coco-api
• cococaption python 3
• cider

Argument Parser is currently not supported. We will add support to it soon.

You can download the pretrained models from here. Place them in eval folder.

In this work, we use 36 fixed bottom-up features. If you wish to use the adaptive features (10-100), please refer to adaptive_features folder in this repository and follow the instructions.

First, download the fixed features from here and unzip the file. Place the unzipped folder in bottom-up_features folder.

Next type this command:

python bottom-up_features/tsv.py

This command will create the following files:

• An HDF5 file containing the bottom up image features for train and val splits, 36 per image for each split, in an (I, 36, 2048) tensor where I is the number of images in the split.
• PKL files that contain training and validation image IDs mapping to index in HDF5 dataset created above.

You can either download all the related caption data files from here or create them yourself. The folder contains the following:

• WORDMAP_coco: maps the words to indices
• CAPUTIL: stores the information about the existing captions in a dictionary organized as follows: {"COCO_image_name": {"caption": "existing caption to be edited", "encoded_previous_caption": an encoded list of the words, "previous_caption_length": a list contaning the length of the caption, "image_ids": the COCO image id}
• CAPTIONS the encoded ground-truth captions (a list with number_images x 5 lists. Example: we have 113,287 training images in Karpathy Split, thereofre there is 566,435 lists for the training split)
• CAPLENS: the length of the ground-truth captions (a list with number_images x 5 vallues)
• NAMES: the COCO image name in the same order as the CAPTIONS
• GENOME_DETS: the splits and image ids for loading the images in accordance to the features file created above

If you'd like to create the caption data yourself, download Karpathy's Split training, validation, and test splits. This zip file contains the captions. Place the file in caption data folder. You should also have the pkl files created from the 'Download Features' section: train36_imgid2idx.pkl and val36_imgid2idx.pkl.

Next, run:

python preprocess_caps.py

This will dump all the files to the folder caption data.

Next, download the existing captios to be edited, and organize them in a list containing dictionaries with each dictionary in the following format: {"image_id": COCO_image_id, "caption": "caption to be edited", "file_name": "split\\COCO_image_name"}. For example: {"image_id": 522418, "caption": "a woman cutting a cake with a knife", "file_name": "val2014\\COCO_val2014_000000522418.jpg"}. In our work, we use the captions produced by AoANet.

Next, run:

python preprocess_existing_caps.py

This will dump all the existing caption files to the folder caption data.

Download the RL-data for sequence-level training used for computing metric scores from here.

Alternitavely, you may prepare the data yourself:

Run the following command:

python preprocess_rl.py

This will dump two files in the data folder used for computing metric scores.

For training DCNet, run:

python dcnet.py

For optimizing DCNet with MSE, run:

python dcnet_with_mse.py

For training editnet:

python editnet.py

For training DCNet, run:

python dcnet_rl.py

For training editnet:

python editnet_rl.py

Refer to eval folder for instructions. All the generated captions and scores from our model can be found in the outputs folder.

@InProceedings{Sammani_2020_CVPR,
author = {Sammani, Fawaz and Melas-Kyriazi, Luke},
title = {Show, Edit and Tell: A Framework for Editing Image Captions},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2020}
}

Our code is mainly based on self-critical and show attend and tell. We thank both authors.