MART: Memory-Augmented Recurrent Transformer for Coherent Video Paragraph Captioning

PyTorch code for our ACL 2020 paper "MART: Memory-Augmented Recurrent Transformer for Coherent Video Paragraph Captioning" by Jie Lei, Liwei Wang, Yelong Shen, Dong Yu, Tamara L. Berg, and Mohit Bansal

Generating multi-sentence descriptions for videos is one of the most challenging captioning tasks due to its high requirements for not only visual relevance but also discourse-based coherence across the sentences in the paragraph. Towards this goal, we propose a new approach called Memory-Augmented Recurrent Transformer (MART), which uses a memory module to augment the transformer architecture. The memory module generates a highly summarized memory state from the video segments and the sentence history so as to help better prediction of the next sentence (w.r.t. coreference and repetition aspects), thus encouraging coherent paragraph generation. Extensive experiments, human evaluations, and qualitative analyses on two popular datasets ActivityNet Captions and YouCookII show that MART generates more coherent and less repetitive paragraph captions than baseline methods, while maintaining relevance to the input video events.

Related works:

• TVC (Video+Dialogue Captioning).
• TVR (Video+Dialogue Retrieval).
• TVQA (Localized Video QA).
• TVQA+ (Spatio-Temporal Video QA).

Getting started

Prerequisites

0. Clone this repository

# no need to add --recursive as all dependencies are copied into this repo.
git clone https://github.com/jayleicn/recurrent-transformer.git
cd recurrent-transformer

1. Prepare feature files

Download features from Google Drive: rt_anet_feat.tar.gz (39GB) and rt_yc2_feat.tar.gz (12GB). These features are repacked from features provided by densecap.

mkdir video_feature && cd video_feature
tar -xf path/to/rt_anet_feat.tar.gz 
tar -xf path/to/rt_yc2_feat.tar.gz 

2. Install dependencies

• Python 2.7
• PyTorch 1.1.0
• nltk
• easydict
• tqdm
• tensorboardX

3. Add project root to PYTHONPATH

source setup.sh

Note that you need to do this each time you start a new session.

Training and Inference

We give examples on how to perform training and inference with MART.

0. Build Vocabulary

bash scripts/build_vocab.sh DATASET_NAME

DATASET_NAME can be anet for ActivityNet Captions or yc2 for YouCookII.

1. MART training

The general training command is:

bash scripts/train.sh DATASET_NAME MODEL_TYPE

MODEL_TYPE can be one of [mart, xl, xlrg, mtrans, mart_no_recurrence], see details below.

MODEL_TYPE	Description
mart	Memory Augmented Recurrent Transformer
xl	Transformer-XL
xlrg	Transformer-XL with recurrent gradient
mtrans	Vanilla Transformer
mart_no_recurrence	mart with recurrence disabled

To train our MART model on ActivityNet Captions:

bash scripts/train.sh anet mart

Training log and model will be saved at results/anet_re_*.
Once you have a trained model, you can follow the instructions below to generate captions.

2. Generate captions

bash scripts/translate_greedy.sh anet_re_* val

Replace anet_re_* with your own model directory name. The generated captions are saved at results/anet_re_*/greedy_pred_val.json

3. Evaluate generated captions

bash scripts/eval.sh anet val results/anet_re_*/greedy_pred_val.json

The results should be comparable with the results we present at Table 2 of the paper. E.g., B@4 10.33; R@4 5.18.

Citations

If you find this code useful for your research, please cite our paper:

@inproceedings{lei2020mart,
  title={MART: Memory-Augmented Recurrent Transformer for Coherent Video Paragraph Captioning},
  author={Lei, Jie and Wang, Liwei and Shen, Yelong and Yu, Dong and Berg, Tamara L and Bansal, Mohit},
  booktitle={ACL},
  year={2020}
}

Others

This code used resources from the following projects: transformers, transformer-xl, densecap, OpenNMT-py.

Contact

jielei [at] cs.unc.edu