Dual Encoding for Zero-Example Video Retrieval

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

Requirements

Environments

Ubuntu 16.04
CUDA 9.0
Python 2.7
PyTorch 0.3.1

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

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:

Generate a vocabulary on the training set.
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.
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@1	R@5	R@10	Med r	mAP
Text-to-Video	7.6	22.4	31.8	33	0.155
Video-to-Text	12.8	30.3	42.4	16	0.065

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

FeatureData: video frame features. Using txt2bin.py to convert video frame feature in the required binary format.
${subset_name}.txt: all video IDs in the specific subset, one video ID per line.
${dsubset_name}.caption.txt: caption data. The file structure is as follows, in which the video and sent in the same line are relevant.

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},
}

chenchengfd/dual_encoding