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Video Re-localization

by Yang Feng, Lin Ma, Wei Liu, Tong Zhang, Jiebo Luo

Introduction

Video Re-localization aims to accurately localize a segment in a reference video such that the segment semantically corresponds to a query video. For more details, please refer to our paper.

alt text

Citation

@InProceedings{feng2018video,
  author = {Feng, Yang and Ma, Lin and Liu, Wei and Zhang, Tong and Luo, 
  Jiebo},
  title = {Video Re-localization},
  booktitle = {ECCV},
  year = {2018}
}

Requirements

sudo apt install python-opencv
pip install tensorflow-gpu==1.13.1

Dataset

  1. Download the ActivityNet features at link. You will get activitynet_v1-3.part-00 to activitynet_v1-3.part-05.

  2. Merge and unzip the files. You'll get sub_activitynet_v1-3.c3d.hdf5.

    cat activitynet_v1-3.part-0* > temp.zip
    unzip temp.zip
    
  3. Get the code and split the features.

    git clone https://github.com/fengyang0317/video_reloc.git
    cd video_reloc
    ln -s /path/to/sub_activitynet_v1-3.c3d.hdf5 data/
    python split_feat.py
    
  4. [Optional] Download the all the videos into data/videos and get the number of frames in each video.

    python get_frame_num.py
    
  5. Generate the dataset json.

    python create_dataset.py
    

Model

  1. Train the model.

    python match.py --data_dir data
    
  2. Eval the model. The results may be slightly different from the values reported in the paper.

    python eval.py --data_dir data --ckpt_path saving/model.ckpt-(best val ckpt)
    

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Code for "Video Re-localization" in ECCV 2018

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