SODA

This repository is the imprimentation of "SODA: Story Oriented Dense Video Captioning Evaluation Flamework" published at ECCV 2020 pdf. SODA measures the performance of video story description systems.

Update

v1.1 (2021/5)

• Added new option "--multi_reference" to deal with multiple reference.
  SODA selects the reference that has the maximum f1 for each video, and returns macro averaged scores.
• Fixed BertScore import

Requirements

python 3.6+ (developed with 3.7)

• Numpy
• tqdm
• pycocoevalcap (Python3 version)
• BERTScore (optional)

Usage

You can run SODA by specifying the path of system output and that of ground truth. Both files should be the json format for ActivityNet Captions.

python soda.py -s path/to/submission.json -r path/to/ground_truth.json 

You can run on the multiple reference setting, with --multi_reference option.

python soda.py --multi_reference -s path/to/submission.json -r path/to/ground_truth1.json path/to/ground_truth2.json

You can try other sentence evaluation metrics, e.g. CIDEr and BERTScore, with -m option.

python soda.py -s path/to/submission.json -m BERTScore

Sample input file

Please use the same format as ActivityNet Challenge

{
  version: "VERSION 1.0",
  results: {
    "sample_id" : [
        {
        sentence: "This is a sample caption.",
        timestamp: [1.23, 4.56]
        },
        {
        sentence: "This is a sample caption 2.",
        timestamp: [7.89, 19.87]
        }
    ]
  }
  external_data: {
    used: False,
    }
}

Reference

@inproceedings{Fujita2020soda,
  title={SODA: Story Oriented Dense Video Captioning Evaluation Flamework},
  author={Soichiro Fujita and Tsutomu Hirao and Hidetaka Kamigaito and Manabu Okumura and Masaaki Nagata},
  booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},
  month={August},
  year={2020},
}

LICENSE

NTT License

According to the license, it is not allowed to create pull requests. Please feel free to send issues.