WSDEC

This is the official repo for our NeurIPS paper Weakly Supervised Dense Event Captioning in Videos.

Description

Repo directories

./: global config files, training, evaluating scripts;
./data: data dictionary;
./model: our final models used to reproduce the results;
./runs: the default output dictionary used to store our trained model and result files;
./scripts: helper scripts;
./third_party: third party dependency include the official evaluation scripts;
./utils: helper functions;
./train_script: all training scripts;
./eval_script: all evalulating scripts.

Dependency

Python 2.7
CUDA 9.0(note: you will encounter a bug saying segmentation fault(core dump) if you run our code with CUDA 8.0)
- But it seems that the bug still exists. See issue
[Pytorch 0.3.1](note: 0.3.1 is not compatible with newer version)
numpy, hdf5 and other necessary packages(no special requirement)

Usage for reproduction

Before we start

Before the training and testing, we should make sure the data, third party data are prepared, here is the one-by-one steps to make everything prepared.

1. Clone our repo and submodules

git clone --recursive https://github.com/XgDuan/WSDEC

2. Download all the data

Download the official C3D features, you can either download the data from the website or from our onedrive cloud.
- Download from the official website; (Note, after you download the C3D features, you can either place it in the data folder and rename it as anet_v1.3.c3d.hdf5, or create a soft link in the data dictionary as ln -s YOURC3DFeature data/anet_v1.3.c3d.hdf5)
Download the dense video captioning data from the official website; (Similar to the C3D feature, you are supposed to place the download data in the data folder and rename it as densecap)
Download the data for the official evaluation scripts densevid_eval;
- run the command sh download.sh scripts in the folder PREFIX/WSDEC/third_party/densevid_eval;
[Good News]: we write a shell script for you to download the data, just run the following command:
```
cd data
sh download.sh
```

3. Generate the dictionary for the caption model

python scripts/caption_preprocess.py

Training

There are two steps for model training: pretrain a not so bad caption model; and the second step, train the final/baseline model.

Our pretrained captioning model is trained.

python train_script/train_cg_pretrain.py

train our final model

python train_script/train_final.py --checkpoint_cg YOUR_PRETRAINED_CAPTION_MODEL.ckp --alias MODEL_NAME

train baselines

train the baseline model without classification loss.

python train_script/train_baseline_regressor.py --checkpoint_cg YOUR_PRETRAINED_CAPTION_MODEL.ckp --alias MODEL_NAME

train the baseline model without regression branch.

python train_script/train_final.py --checkpoint_cg YOUR_PRETRAINED_CAPTION_MODEL.ckp --regressor_scale 0 --alias MODEL_NAME

About the arguments

All the arguments we use can be found in the corresponding training scripts. You can also use your own argumnets if you like to do so. But please mind, some arguments are discarded(This is our own reimplementation of our paper, the first version codes are too dirty that no one would like to use it.)

Testing

Testing is easier than training. Firstly, in the process of training, our scripts will call the densevid_eval in a subprocess every time after we run the eval function. From these results, you can have a general grasp about the final performance by just have a look at the eval_results.txt scripts. Secondly, after some epochs, you can run the evaluation scripts:

evaluate the full model or no_regression model:

python eval_script/evaluate.py --checkpoint YOUR_TRAINED_MODEL.ckp

evaluate the no_classification model:

python eval_script/evaluate_baseline_regressor.py --checkpoint YOUR_TRAINED_MODEL.ckp

evaluate the pretrained model with random temporal segment:

python eval_script/evaluate_pretrain.py --checkpoint YOUR_PRETRAIN_CAPTION_MODEL.ckp

Other usages

Besides reproduce our work, there are at least two interesting things you can do with our codes.

Train a supervised sentence localization model

To know what is sentence localization, you can have a look at our paper ABLR. Note that our work at a matter of fact provides an unsupervised solution towards sentence localization, we introduce the usage for the supervised model here. We have written the trainer, you can just run the following command and have a cup of coffee:

python train_script/train_sl.py

Train a supervised video event caption generation model

If you have read our paper, you would find that event captioning is the dual task of the aforementioned sentence localization task. To train such a model, just run the following command:

python train_script/train_cg.py

BUGS

You may encounter a cuda internal bug that says Segmentation fault(core dumped) during training if you are using cuda 8.0. If such things happen, try upgrading your cuda to 9.0.

other

We will add more description about how to use our code. Please feel free to contact us if you have any questions or suggestions.

Trained model and results

Links for our trained model

You can download our pretrained model for evaluation or further usage from our onedrive, which includes a pretrained caption generator(cg_pretrain.ckp), a baseline model without classification loss(baseline_noclass.ckp), a baseline model without regression branch(baseline_noregress.ckp), and our final model(final_model.ckp).

Cite the paper and give us star⭐️

If you find our paper or code useful, please cite our paper using the following bibtex:

@incollection{NIPS2018_7569,
title = {Weakly Supervised Dense Event Captioning in Videos},
author = {Duan, Xuguang and Huang, Wenbing and Gan, Chuang and Wang, Jingdong and Zhu, Wenwu and Huang, Junzhou},
booktitle = {Advances in Neural Information Processing Systems 31},
editor = {S. Bengio and H. Wallach and H. Larochelle and K. Grauman and N. Cesa-Bianchi and R. Garnett},
pages = {3062--3072},
year = {2018},
publisher = {Curran Associates, Inc.},
url = {http://papers.nips.cc/paper/7569-weakly-supervised-dense-event-captioning-in-videos.pdf}
}