Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

https://github.com/danieljf24/dual_encoding

[CVPR2019] Dual Encoding for Zero-Example Video Retrieval
https://github.com/danieljf24/dual_encoding

Last synced: 3 months ago
JSON representation

[CVPR2019] Dual Encoding for Zero-Example Video Retrieval

Lists

README 

        
# Dual Encoding for Zero-Example Video Retrieval

Source code of our CVPR'19  paper [Dual Encoding for Zero-Example Video Retrieval](https://arxiv.org/abs/1809.06181).



**Note an improved video-text retrieval model is available [here](https://github.com/danieljf24/hybrid_space).**



![image](dual_encoding.jpg)



## Requirements



#### Environments

* **Ubuntu** 16.04

* **CUDA** 9.0

* **Python** 2.7 (For python 3, please checkout `python3` branch)

* **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.

```shell

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)](http://lixirong.net/data/cvpr2019/msrvtt10k-text-and-resnet-152-img1k.tar.gz) dataset and a pre-trained [word2vec(3.0G)](http://lixirong.net/data/w2vv-tmm2018/word2vec.tar.gz).

The extracted data is placed in `$HOME/VisualSearch/`.

```shell

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](https://drive.google.com/drive/folders/1GoomucXoAmhd3Jhngdnea7t0GOnJoGth?usp=sharing) and [Baidu Pan](https://pan.baidu.com/s/1Z5wgpZQPL2YZakGJsD1Khg).

Note: Code of video feature extraction is available [here](https://github.com/xuchaoxi/video-cnn-feat).



## Getting started

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

```shell

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)](http://lixirong.net/data/cvpr2019/model_best.pth.tar)  on MSR-VTT.

```shell

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 |



## 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](https://drive.google.com/drive/folders/1GoomucXoAmhd3Jhngdnea7t0GOnJoGth?usp=sharing) and [Baidu Pan](https://pan.baidu.com/s/1Z5wgpZQPL2YZakGJsD1Khg).  

Run the following script to train and evaluate `Dual Encoding` network on MSVD. 

```shell

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@1 | R@5 | R@10 | Med r |	mAP |

| ------------- | ------------- | ------------- | ------------- |  ------------- | ------------- |

| Text-to-Video | 12.7 | 34.5 | 46.4 | 13 | 0.234 |

| Video-to-Text | 16.1 | 32.1 | 41.5 | 17 | 0.112 |



## 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

* Sentences: [tgif-msrvtt10k](http://lixirong.net/data/mm2019/tgif-msrvtt10k-sent.tar.gz), [tv2016train](http://lixirong.net/data/mm2019/tv2016train-sent.tar.gz)

* TRECVID 2016 / 2017 / 2018 AVS topics and ground truth:  [iacc.3](http://lixirong.net/data/mm2019/iacc.3-avs-topics.tar.gz)



#### Frame-level feature data

* 2048-dim ResNeXt-101: [tgif](http://39.104.114.128/avs/tgif_ResNext-101.tar.gz)(7G), [msrvtt10k](http://39.104.114.128/avs/msrvtt10k_ResNext-101.tar.gz)(2G), [tv2016train](http://39.104.114.128/avs/tv2016train_ResNext-101.tar.gz)(42M), [iacc.3](http://39.104.114.128/avs/iacc.3_ResNext-101.tar.gz)(27G)



```shell

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



```shell

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 



deactive

```



## How to run Dual Encoding on another datasets?



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

```shell

${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](https://github.com/danieljf24/simpleknn/blob/master/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:

```shell

./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:

```shell

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.

```shell

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},

}

```