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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",
      ...],
  },
  ....
}
[
  {
    "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

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)