Weakly-Supervised Spatio-Temporally Grounding Natural Sentence in Video

This repo contains the main baselines of VID-sentence dataset introduced in WSSTG. Please refer to our paper and the repo for the information of VID-sentence dataset.

Task

Description: "A brown and white dog is lying on the grass and then it stands up."

The proposed WSSTG task aims to localize a spatio-temporal tube (i.e., the sequence of green bounding boxes) in the video which semantically corresponds to the given sentence, with no reliance on any spatio-temporal annotations during training.

Architecture

The architecture of the proposed method.

Contents

1. Requirements: software
2. Installation
3. Training
4. Testing

Requirements: software

• Pytorch (version=0.4.0)
• python 2.7
• numpy
• scipy
• magic
• easydict
• dill
• matplotlib
• tensorboardX

Installation

1. Clone the WSSTG repository and VID-sentence reposity

    git clone https://github.com/JeffCHEN2017/WSSTG.git
    git clone https://github.com/JeffCHEN2017/VID-Sentence.git
    ln -s VID-sentence_ROOT/data/ILSVRC WSSTG_ROOT/data

2. Download tube proposals, RGB feature and I3D feature from Google Drive.

3. Extract *.tar files and make symlinks between the download data and the desired data folder

tar xvf tubePrp.tar
ln -s tubePrp $WSSTG_ROOT/data/tubePrp

tar xvf vid_i3d.tar vid_i3d
ln -s vid_i3d $WSSTG_ROOT/data/vid_i3d
ln -s $WSSTG_ROOT/data/vid_i3d/val test

tar xvf vid_rgb.tar vid_rgb
ln -s vid_rgb $WSSTG_ROOT/data/vid_rgb
ln -s $WSSTG_ROOT/data/vid_rgb/vidTubeCacheFtr/val test

Note: We extract the tube proposals using the method proposed by Gkioxari and Malik .A python implementation here is provided by Yamaguchi etal.. We extract singel-frame propsoals and RGB feature for each frame using a faster-RCNN model pretrained on COCO dataset, which is provided by Jianwei Yang. We extract I3D-RGB and I3D-flow features using the model provided by Carreira and Zisserman.

Training

cd $WSSTG_ROOT
sh scripts/train_video_emb_att.sh

Notice: Because the changes of batch sizes and the random seed, the performance may be slightly different from our submission. We provide a checkpoint here which achieves similar performance (38.1 VS 38.2 on the [email protected] ) to the model we reported in the paper.

Testing

Download the checkpoint from Google Drive, put it in WSSTG_ROOT/data/models and run

cd $WSSTG_ROOT
sh scripts/test_video_emb_att.sh

License

WSSTG is released under the CC-BY-NC 4.0 LICENSE (refer to the LICENSE file for details).

Citing WSSTG

If you find this repo useful in your research, please consider citing:

@inproceedings{chen2019weakly,
    Title={Weakly-Supervised Spatio-Temporally Grounding Natural Sentence in Video},
    Author={Chen, Zhenfang and Ma, Lin and Luo, Wenhan and Wong, Kwan-Yee~K},
    Booktitle={ACL},
    year={2019}
}

Contact

You can contact Zhenfang Chen by sending email to [email protected]