Transform and Tell: Entity-Aware News Image Captioning

This repository contains the code to reproduce the results in our CVPR 2020 paper Transform and Tell: Entity-Aware News Image Captioning. We propose an end-to-end model which generates captions for images embedded in news articles. News images present two key challenges: they rely on real-world knowledge, especially about named entities; and they typically have linguistically rich captions that include uncommon words. We address the first challenge by associating words in the caption with faces and objects in the image, via a multi-modal, multi-head attention mechanism. We tackle the second challenge with a state-of-the-art transformer language model that uses byte-pair-encoding to generate captions as a sequence of word parts.

On the GoodNews dataset, our model outperforms the previous state of the art by a factor of four in CIDEr score (13 to 54). This performance gain comes from a unique combination of language models, word representation, image embeddings, face embeddings, object embeddings, and improvements in neural network design. We also introduce the NYTimes800k dataset which is 70% larger than GoodNews, has higher article quality, and includes the locations of images within articles as an additional contextual cue.

A live demo can be accessed here. In the demo, you can provide the URL to a New York Times article. The server will then scrape the web page, extract the article and image, and feed them into our model to generate a caption.

Please cite with the following BibTeX:

@InProceedings{Tran_2020_CVPR,
  author = {Tran, Alasdair and Mathews, Alexander and Xie, Lexing},
  title = {Transform and Tell: Entity-Aware News Image Captioning},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  month = {June},
  year = {2020}
}

Requirements

# Install Anaconda for Python and then create a dedicated environment.
# This will make it easier to reproduce our experimental numbers.
conda env create -f environment.yml
conda activate tell

# This step is only needed if you want to use the Jupyter notebook
python -m ipykernel install --user --name tell --display-name "tell"

# Our Pytorch uses CUDA 10.2. Ensure that CUDA_HOME points to the right
# CUDA version. Chagne this depending on where you installed CUDA.
export CUDA_HOME=/usr/local/cuda-10.2

# We also pin the apex version, which is used for mixed precision training
cd libs/apex
git submodule init && git submodule update .
pip install -v --no-cache-dir --global-option="--pyprof" --global-option="--cpp_ext" --global-option="--cuda_ext" ./

# Install our package
cd ../.. && python setup.py develop

# Spacy is used to calcuate some of the evaluation metrics
spacy download en_core_web_lg

# We use nltk to tokenize the generated text to compute linguistic metrics
python -m nltk.downloader punkt

Getting Data

The quickest way to get the data is to send an email to [email protected] to request the MongoDB dump that contains the dataset. Alternatively, see here for instructions on how to get the data from scratch, which will take a few days.

Once we have obtained the data from the authors, which consists of two directories expt and data, you can simply put them at the root of this repo.

# If the data is download from our Cloudstor server, then you might need
# to first unzip the archives using either tar or 7z.

# First, let's start an empty local MongoDB server on port 27017. Below
# we set the cache size to 10GB of RAM. Change it depending on your system.
mkdir data/mongodb
mongod --bind_ip_all --dbpath data/mongodb --wiredTigerCacheSizeGB 10

# Next let's restore the NYTimes200k and GoodNews datasets
mongorestore --db nytimes --host=localhost --port=27017 --drop --gzip --archive=data/mongobackups/nytimes-2020-04-21.gz
mongorestore --db goodnews --host=localhost --port=27017 --drop --gzip --archive=data/mongobackups/goodnews-2020-04-21.gz

# Next we unarchive the image directories. For each dataset, you can see two
# directories: `images` and `images_processed`. The files in `images` are
# the orignal files scraped from the New York Times. You only need this
# if you want to recompute the face and object embeddings. Otherwise, all
# the experiments will use the images in `images_processed`, which have
# already been cropped and resized.
tar -zxf data/nytimes/images_processed.tar.gz -C data/nytimes/
tar -zxf data/goodnews/images_processed.tar.gz -C data/goodnews/

# We are now ready to train the models!

You can see an example of how we read the NYTimes800k samples from the MongoDB database here. Here's a minimum working example in Python:

import os
from PIL import Image
from pymongo import MongoClient

# Assume that you've already restored the database and the mongo server is running
client = MongoClient(host='localhost', port=27017)

# All of our NYTimes800k articles sit in the database `nytimes`
db = client.nytimes

# Here we select a random article in the training set.
article = db.articles.find_one({'split': 'train'})

# You can visit the original web page where this article came from
url = article['web_url']

# Each article contains a lot of fields. If you want the title, then
title = article['headline']['main'].strip()

# If you want the article text, then you will need to manually merge all
# paragraphs together.
sections = article['parsed_section']
paragraphs = []
for section in sections:
    if section['type'] == 'paragraph':
        paragraphs.append(section['text'])
article_text = '\n'.join(paragraphs)

# To get the caption of the first image in the article
pos = article['image_positions'][0]
caption = sections[pos]['text'].strip()

# If you want to load the actual image into memory
image_dir = 'data/nytimes/images_processed' # change this accordingly
image_path = os.path.join(image_dir, f"{sections[pos]['hash']}.jpg")
image = Image.open(image_path)

# You can also load the pre-computed FaceNet embeddings of the faces in the image
facenet_embeds = sections[pos]['facenet_details']['embeddings']

# Object embeddings are stored in a separate collection due to a size limit in mongo
obj = db.objects.find_one({'_id': sections[pos]['hash']})
object_embeds = obj['object_features']

Training and Evaluation

# Train the full model on NYTimes800k. This takes around 4 days on a Titan V GPU.
# The training will populate the directory expt/nytimes/9_transformer_objects/serialization
CUDA_VISIBLE_DEVICES=0 tell train expt/nytimes/9_transformer_objects/config.yaml -f

# Once training is finished, the best model weights are stored in
#   expt/nytimes/9_transformer_objects/serialization/best.th
# We can use this to generate captions on the NYTimes800k test set. This
# takes about one hour.
CUDA_VISIBLE_DEVICES=0 tell evaluate expt/nytimes/9_transformer_objects/config.yaml -m expt/nytimes/9_transformer_objects/serialization/best.th

# Compute the evaluation metrics on the test set
python scripts/compute_metrics.py -c data/nytimes/name_counters.pkl expt/nytimes/9_transformer_objects/serialization/generations.jsonl

There are also other model variants which are ablation studies. Check our paper for more details, but here's a summary:

Experiment	Word Embedding	Language Model	Image Attention	Weighted RoBERTa	Location-Aware	Face Attention	Object Attention
1_lstm_glove	GloVe	LSTM	✔
2_transformer_glove	GloVe	Transformer	✔
3_lstm_roberta	RoBERTa	LSTM	✔
4_no_image	RoBERTa	Transformer
5_transformer_roberta	RoBERTa	Transformer	✔
6_transformer_weighted_roberta	RoBERTa	Transformer	✔	✔
7_trasnformer_location_aware	RoBERTa	Transformer	✔	✔	✔
8_transformer_faces	RoBERTa	Transformer	✔	✔	✔	✔
9_transformer_objects	RoBERTa	Transformer	✔	✔	✔	✔	✔

Acknowledgement

• The training and evaluation workflow is based on the AllenNLP framework.

• The Dynamic Convolution architecture is built upon Facebook's fairseq library.

• The ZeroMQ implementation of the demo backend server is based on bert-as-service.

• The front-end of the demo server is created with create-react-app

• ResNet code is adapted from the Pytorch implementation.

• We use Ultralytics' YOLOv3 implementation.

• FaceNet and MTCNN implementations come from here.