FIVR-200K

An annotated dataset of YouTube videos designed as a benchmark for Fine-grained Incident Video Retrieval. The dataset comprises 225,960 videos associated with 4,687 Wikipedia events and 100 selected video queries.

Project Website: [link]

Paper: [publisher] [arXiv] [pdf]

Installation

• Clone this repo:

  git clone https://github.com/MKLab-ITI/FIVR-200K
  cd FIVR-200K

• You can install all the dependencies by

  pip install -r requirements.txt

  or

  conda install --file requirements.txt

• Install yt-dlp (make sure it is up-to-date)

Dataset format

• The files that contains the dataset can be found in dataset folder

• The video annotations are in file annotation.json that has the following format:

  {
  "5MBA_7vDhII": {
  "ND": [
    "_0uCw0B2AgM",
    ...],
  "DS": [
    "hc0XIE1aY0U",
    ...],
  "CS": [
    "ydEqiuDiuyc",
    ...],    
  "IS": [
    "d_ZNjE7B4Wo",
    ...],
  "DA": [
    "rLvVYdtc73Q",
    ...],
  },
  ....
  }

• The events crawled from Wikipedia's Current Event page are in file events.json that has the following format:

  [
  {
  "headline": "iraqi insurgency", 
  "topic": "armed conflict and attack", 
  "date": "2013-01-22", 
  "text": [
    "car bombings in baghdad kill at least 17 people and injure dozens of others."
  ], 
  "href": [
    "http://www.bbc.co.uk/news/world-middle-east-21141242", 
    "https://www.reuters.com/article/2013/01/22/us-iraq-violence-idUSBRE90L0BQ20130122"
  ], 
  "youtube": [
    "ZpjqUq-EnbQ", 
    ...
  ]
  },
  ...
  ]

• The Youtube IDs of the videos in the dataset are in file youtube_ids.txt

• The global features of the benchmarked approaches in the paper can be found here

  • The ordering is according to the youtube_ids.txt

Download Videos

• Run the following command to download videos:

  python download_dataset.py --video_dir VIDEO_DIR [--dataset_ids DATASET_FILE] [--cores NUMBER_OF_CODES] [--resolution RESOLUTION]

• An example to run the download script:

  python download_dataset.py --video_dir ./videos --dataset_ids dataset/youtube_ids.txt --cores 4 --resolution 360

• Videos will be saved in the following directory structure VIDEO_DIR/YT_ID.mp4

• The videos that are no longer available are stored in a text file with name missing_videos.txt

Evaluation

1. Generation of the result file

   • A file that contains a dictionary with keys the YT ids of the query videos and values another dictionary with keys the YT ids of the dataset videos and values their similarity to the query.

   • Results can be stored in a JSON file with the following format:

     {
     "wrC_Uqk3juY": {
     "KQh6RCW_nAo": 0.716,
     "0q82oQa3upE": 0.300,
       ...},
     "k_NT43aJ_Jw": {
     "-KuR8y1gjJQ": 1.0,
     "Xb19O5Iur44": 0.417,
       ...},
     ....
     }

   • An implementation for the generation of the JSON file can be found here

2. Evaluation of the results

   • Run the following command to run the evaluation:

     python evaluation.py --result_file RESULT_FILE --relevant_labels RELEVANT_LABELS

   • An example to run the evaluation script:

     python evaluation.py --result_file ./results/lbow_vgg.json --relevant_labels ND,DS

   • Add flag --help to display the detailed description for the arguments of the evaluation script

3. Evaluation on the three retrieval task

   • Provide different values to the relevant_labels argument to evaluate your results for the three visual-based retrieval task

     DSVR: ND,DS
     CSVR: ND,DS,CS
     ISVR: ND,DS,CS,IS

   • For the Duplicate Audio Video Retrieval (DAVR) task provide DA to the relevant_labels argument

• Reported results

  • To re-produce the results of the paper run the following command

    bash evaluate_run.sh APPROACH_NAME FEATURES_NAME

  • An example to run the evaluation script:

    bash evaluate_run.sh BOW VGG

  • The results will probably not be the same as the reported one in the paper, because we are constantly fixing mislabeled videos that were missed during the annotation process.

  • See in the Updates section when was the last update of the dataset's annotation

Updates

In case that you find a mislabeled video please submit it to the following form here

• Update 21/1/21: add DA label for audio-based annotations of duplicate audio videos
• Update 29/5/19: fix labels for 373 videos

Citation

If you use FIVR-200K dataset for your research, please consider citing our paper:

@article{kordopatis2019fivr,
  title={{FIVR}: Fine-grained Incident Video Retrieval},
  author={Kordopatis-Zilos, Giorgos and Papadopoulos, Symeon and Patras, Ioannis and Kompatsiaris, Ioannis},
  journal={IEEE Transactions on Multimedia},
  year={2019}
}

If you use the audio-based annotations, please also consider citing our paper:

@inproceedings{avgoustinakis2020ausil,
  title={Audio-based Near-Duplicate Video Retrieval with Audio Similarity Learning},
  author={Avgoustinakis, Pavlos and Kordopatis-Zilos, Giorgos and Papadopoulos, Symeon and Symeonidis, Andreas L and Kompatsiaris, Ioannis},
  booktitle={Proceedings of the IEEE International Conference on Pattern Recognition},
  year={2020}
}

Related Projects

Intermediate-CNN-Features - this repo was used to extract our CNN features

NDVR-DML - one of the methods benchmarked in the FIVR-200K dataset

ViSiL - video similarity learning for fine-grained similarity calculation

AuSiL - audio similarity learning for audio-based similarity calculation

License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details

Contact us for further details

Giorgos Kordopatis-Zilos (georgekordopatis@iti.gr)
Symeon Papadopoulos (papadop@iti.gr)