Implementation of our approach for Automatic Understanding of Visual Advertisements Challenge (1st place of 2018 challenge).

You need to have following packages:

• chainer
• chainercv
• keras
• cupy
• gensim
• nltk
• pandas
• pytables
• parse

We also provide a Dockerfile to setup dependencies.

We use Google word2vec to compute word embeddings. Download GoogleNews-vectors-negative300.bin.gz here and set WORD2VEC_PATH.

export WORD2VEC_PATH=/path/to/Word2Vec/GoogleNews-vectors-negative300.bin

You can get the competition dataset here. Downlaod the training/test datasets and extracut them in the data directory.

We also use OCR results. Download the OCR results (figshare) and save in the data directory.

Before training, pre-compute Faster-RCNN features of ad images.

VA_DATASET_ROOT=/path/to/VisualAdvertisementDataset/ python script/save_feat.py

Otherwise, you can download precomputed Faster-RCNN features (figshare), and copy to data/frcnn_feat/.

To train our full model, run

python script/train.py --model_name ocr+vis --text_net cnn

An output directory will be made under /output/checkpoint/, and a trained model and some other output files will be saved in the directory.

To evaluate a model, run

python script/train.py --eval /path/to/output/directory

Download two figshare items Chainer model file and tokenizer and word embeddings (figshare). Copy wordvec.npy and tokenizer.pickle to data directory, then run

python script/train.py --eval /path/to/directory/of/Chainer_model_file

We included some code snippets for visualization. See notebook/visualize inference.ipynb.