neural-storyteller

This project seeks to create a easily accessible UI for uploading images and generating images based on the neural-storyteller project. All props go to their work.

Getting started

You will first need to download some pre-trained models and style vectors. Most of the materials are available in a single compressed file, which you can obtain by running

wget http://www.cs.toronto.edu/~rkiros/neural_storyteller.zip

Included is a pre-trained decoder on romance novels, the decoder dictionary, caption and romance style vectors, MS COCO training captions and a pre-trained image-sentence embedding model.

Next, you need to obtain the pre-trained skip-thoughts encoder. Go here and follow the instructions on the main page to obtain the pre-trained model.

Finally, we need the VGG-19 ConvNet parameters. You can obtain them by running

wget https://s3.amazonaws.com/lasagne/recipes/pretrained/imagenet/vgg19.pkl

Note that this model is for non-commercial use only. Once you have all the materials, open neural/config.py and specify the locations of all of the models and style vectors that you downloaded.

For running on CPU, you will need to download the VGG-19 prototxt and model by:

wget http://www.robots.ox.ac.uk/~vgg/software/very_deep/caffe/VGG_ILSVRC_19_layers.caffemodel
wget https://gist.githubusercontent.com/ksimonyan/3785162f95cd2d5fee77/raw/bb2b4fe0a9bb0669211cf3d0bc949dfdda173e9e/VGG_ILSVRC_19_layers_deploy.prototxt

You also need to modify pycaffe and model path in neural/config.py, and modify the flag in line 8 as:

FLAG_CPU_MODE = True

install

pip install -r requirements.txt
cd web && npm install

run

npm run start:server

or

npm start // Frontend app only

The original neural-storyteller

neural-storyteller is a recurrent neural network that generates little stories about images. This repository contains code for generating stories with your own images, as well as instructions for training new models.

*We were barely able to catch the breeze at the beach , and it felt as if someone stepped out of my mind . She was in love with him for the first time in months , so she had no intention of escaping . The sun had risen from the ocean , making her feel more alive than normal . She 's beautiful , but the truth is that I do n't know what to do . The sun was just starting to fade away , leaving people scattered around the Atlantic Ocean . I d seen the men in his life , who guided me at the beach once more .*

Samim has made an awesome blog post with lots of results here.

Some more results from an older model trained on Adventure books can be found here.

The whole approach contains 4 components:

• skip-thought vectors
• image-sentence embeddings
• conditional neural language models
• style shifting (described in this project)

The 'style-shifting' operation is what allows our model to transfer standard image captions to the style of stories from novels. The only source of supervision in our models is from Microsoft COCO captions. That is, we did not collect any new training data to directly predict stories given images.

Style shifting was inspired by A Neural Algorithm of Artistic Style but the technical details are completely different.

How does it work?

We first train a recurrent neural network (RNN) decoder on romance novels. Each passage from a novel is mapped to a skip-thought vector. The RNN then conditions on the skip-thought vector and aims to generate the passage that it has encoded. We use romance novels collected from the BookCorpus dataset.

Parallel to this, we train a visual-semantic embedding between COCO images and captions. In this model, captions and images are mapped into a common vector space. After training, we can embed new images and retrieve captions.

Given these models, we need a way to bridge the gap between retrieved image captions and passages in novels. That is, if we had a function F that maps a collection of image caption vectors x to a book passage vector F(x), then we could feed F(x) to the decoder to get our story. There is no such parallel data, so we need to construct F another way.

It turns out that skip-thought vectors have some intriguing properties that allow us to construct F in a really simple way. Suppose we have 3 vectors: an image caption x, a "caption style" vector c and a "book style" vector b. Then we define F as

F(x) = x - c + b

which intuitively means: keep the "thought" of the caption, but replace the image caption style with that of a story. Then, we simply feed F(x) to the decoder.

How do we construct c and b? Here, c is the mean of the skip-thought vectors for Microsoft COCO training captions. We set b to be the mean of the skip-thought vectors for romance novel passages that are of length > 100.

License

all distribution, reproduct and usage must follow the neural-storyteller terms and conditions.