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Dual Encoding for Zero-Example Video Retrieval

Source code of our CVPR'19 paper Dual Encoding for Zero-Example Video Retrieval.

Note an improved video-text retrieval model is available here.

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Requirements

Environments

We used virtualenv to setup a deep learning workspace that supports PyTorch. Run the following script to install the required packages.

virtualenv --system-site-packages -p python2.7 ~/ws_dual
source ~/ws_dual/bin/activate
git clone https://github.com/danieljf24/dual_encoding.git
cd ~/dual_encoding
pip install -r requirements.txt
deactivate

Required Data

Run do_get_dataset.sh or the following script to download and extract MSR-VTT(1.9G) dataset and a pre-trained word2vec(3.0G). The extracted data is placed in $HOME/VisualSearch/.

ROOTPATH=$HOME/VisualSearch
mkdir -p $ROOTPATH && cd $ROOTPATH

# download and extract dataset
wget http://lixirong.net/data/cvpr2019/msrvtt10k-text-and-resnet-152-img1k.tar.gz
tar zxf msrvtt10k-text-and-resnet-152-img1k.tar.gz

# download and extract pre-trained word2vec
wget http://lixirong.net/data/w2vv-tmm2018/word2vec.tar.gz
tar zxf word2vec.tar.gz

The data can also be downloaded from Google Drive and Baidu Pan. Note: Code of video feature extraction is available here.

Getting started

Run the following script to train and evaluate Dual Encoding network on MSR-VTT.

source ~/ws_dual/bin/activate
./do_all.sh msrvtt10ktrain msrvtt10kval msrvtt10ktest full
deactive

Running the script will do the following things:

  1. Generate a vocabulary on the training set.
  2. Train Dual Encoding network and select a checkpoint that performs best on the validation set as the final model. Notice that we only save the best-performing checkpoint on the validation set to save disk space.
  3. Evaluate the final model on the test set.

Expected Performance

Run the following script to evaluate our trained model(302M) on MSR-VTT.

source ~/ws_dual/bin/activate
MODELDIR=$HOME/VisualSearch/msrvtt10ktrain/cvpr_2019
mkdir -p $MODELDIR
wget -P $MODELDIR http://lixirong.net/data/cvpr2019/model_best.pth.tar
CUDA_VISIBLE_DEVICES=0 python tester.py msrvtt10ktest --logger_name $MODELDIR
deactive

The expected performance of Dual Encoding on MSR-VTT is as follows. Notice that due to random factors in SGD based training, the numbers differ slightly from those reported in the paper.

R@1R@5R@10Med rmAP
Text-to-Video7.622.431.8330.155
Video-to-Text12.830.342.4160.065

Dual Encoding on MSVD

The msvd dataset (msvd-text-and-resnet-152-img1k.tar.gz) with extracted video feature can be downloaded from Google Drive and Baidu Pan.
Run the following script to train and evaluate Dual Encoding network on MSVD.

source ~/ws_dual/bin/activate
./do_all.sh msvdtrain msvdval msvdtest full
deactive

We utilized 1,200, 100 and 670 video clips for training, validation, and test. All the sentences associated with videos are used. The performance are shown in the following.

R@1R@5R@10Med rmAP
Text-to-Video12.734.546.4130.234
Video-to-Text16.132.141.5170.112

Dual Encoding on Ad-hoc Video Search (AVS)

Data

The following three datasets are used for training, validation and testing: tgif-msrvtt10k, tv2016train and iacc.3. For more information about these datasets, please refer to https://github.com/li-xirong/avs.

Run the following scripts to download and extract these datasets. The extracted data is placed in $HOME/VisualSearch/.

Sentence data

Frame-level feature data

ROOTPATH=$HOME/VisualSearch
cd $ROOTPATH

# download and extract dataset
wget http://39.104.114.128/avs/tgif_ResNext-101.tar.gz
tar zxvf tgif_ResNext-101.tar.gz

wget http://39.104.114.128/avs/msrvtt10k_ResNext-101.tar.gz
tar zxvf msrvtt10k_ResNext-101.tar

wget http://39.104.114.128/avs/tv2016train_ResNext-101.tar.gz
tar zxvf tv2016train_ResNext-101.tar.gz

wget http://39.104.114.128/avs/iacc.3_ResNext-101.tar.gz
tar zxvf iacc.3_ResNext-101.tar.gz

# combine feature of tgif and msrvtt10k
./do_combine_features.sh

Train Dual Encoding model from scratch

source ~/ws_dual/bin/activate

trainCollection=tgif-msrvtt10k
visual_feature=pyresnext-101_rbps13k,flatten0_output,os

# Generate a vocabulary on the training set
./do_get_vocab.sh $trainCollection

# Generate video frame info
./do_get_frameInfo.sh $trainCollection $visual_feature


# training and testing
./do_all_avs.sh 

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How to run Dual Encoding on another datasets?

Store the training, validation and test subset into three folders in the following structure respectively.

${subset_name}
├── FeatureData
│   └── ${feature_name}
│       ├── feature.bin
│       ├── shape.txt
│       └── id.txt
├── ImageSets
│   └── ${subset_name}.txt
└── TextData
    └── ${subset_name}.caption.txt

video_id_1#1 sentence_1
video_id_1#2 sentence_2
...
video_id_n#1 sentence_k
...

You can run the following script to check whether the data is ready:

./do_format_check.sh ${train_set} ${val_set} ${test_set} ${rootpath} ${feature_name}

where train_set, val_set and test_set indicate the name of training, validation and test set, respectively, ${rootpath} denotes the path where datasets are saved and feature_name is the video frame feature name.

If you pass the format check, use the following script to train and evaluate Dual Encoding on your own dataset:

source ~/ws_dual/bin/activate
./do_all_own_data.sh ${train_set} ${val_set} ${test_set} ${rootpath} ${feature_name} ${caption_num} full
deactive

where caption_num denotes the number of captions for each video. For the MSRVTT dataset, the value of caption_num is 20.

If training data of your task is relatively limited, we suggest dual encoding with level 2 and 3. Compared to the full edition, this version gives nearly comparable performance on MSR-VTT, but with less trainable parameters.

source ~/ws_dual/bin/activate
./do_all_own_data.sh ${train_set} ${val_set} ${test_set} ${rootpath} ${feature_name} ${caption_num} reduced
deactive

References

If you find the package useful, please consider citing our CVPR'19 paper:

@inproceedings{cvpr2019-dual-dong,
title = {Dual Encoding for Zero-Example Video Retrieval},
author = {Jianfeng Dong and Xirong Li and Chaoxi Xu and Shouling Ji and Yuan He and Gang Yang and Xun Wang},
booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2019},
}