metadata

license: apache-2.0
task_categories:
  - summarization
language:
  - en
tags:
  - cross-modal-video-summarization
  - video-summarization
  - video-captioning
pretty_name: VideoXum
size_categories:
  - 10K<n<100K

Dataset Card for VideoXum

Table of Contents
Dataset Description
- Dataset Summary
- Languages
Dataset Structure
Citation

Dataset Description

Homepage: https://videoxum.github.io/
Paper: https://arxiv.org/abs/2303.12060

Dataset Summary

The VideoXum dataset represents a novel task in the field of video summarization, extending the scope from single-modal to cross-modal video summarization. This new task focuses on creating video summaries that containing both visual and textual elements with semantic coherence. Built upon the foundation of ActivityNet Captions, VideoXum is a large-scale dataset, including over 14,000 long-duration and open-domain videos. Each video is paired with 10 corresponding video summaries, amounting to a total of 140,000 video-text summary pairs.

Languages

The textual summarization in the dataset are in English.

Dataset Structure

Dataset Splits

	train	validation	test	Overall
# of videos	8,000	2,001	4,000	14,001

Dataset Resources

train_videoxum.json: annotations of training set
val_videoxum.json: annotations of validation set
test_videoxum.json: annotations of test set

Dataset Fields

video_id: str a unique identifier for the video.
duration: float total duration of the video in seconds.
sampled_frames: int the number of frames sampled from source video at 1 fps with a uniform sampling schema.
timestamps: List_float a list of timestamp pairs, with each pair representing the start and end times of a segment within the video.
tsum: List_str each textual video summary provides a summarization of the corresponding video segment as defined by the timestamps.
vsum: List_float each visual video summary corresponds to key frames within each video segment as defined by the timestamps. The dimensions (3 x 10) suggest that each video segment was reannotated by 10 different workers.
vsum_onehot: List_bool one-hot matrix transformed from 'vsum'. The dimensions (10 x 83) denotes the one-hot labels spanning the entire length of a video, as annotated by 10 workers.

Annotation Sample

For each video, We hire workers to annotate ten shortened video summaries.

{
    'video_id': 'v_QOlSCBRmfWY',
    'duration': 82.73,
    'sampled_frames': 83
    'timestamps': [[0.83, 19.86], [17.37, 60.81], [56.26, 79.42]],
    'tsum': ['A young woman is seen standing in a room and leads into her dancing.',
             'The girl dances around the room while the camera captures her movements.',
             'She continues dancing around the room and ends by laying on the floor.'],
    'vsum': [[[ 7.01, 12.37], ...],
             [[41.05, 45.04], ...],
             [[65.74, 69.28], ...]] (3 x 10 dim)
    'vsum_onehot': [[[0,0,0,...,1,1,...], ...],
                    [[0,0,0,...,1,1,...], ...],
                    [[0,0,0,...,1,1,...], ...],] (10 x 83 dim)
}

File Structure of Dataset

The file structure of VideoXum looks like:

dataset
└── ActivityNet
    ├── anno
    │   ├── test_videoxum.json
    │   ├── train_videoxum.json
    │   └── val_videoxum.json
    └── feat
        ├── blip
        │   ├── v_00Dk03Jr70M.npz
        │   └── ...
        └── vt_clipscore
            ├── v_00Dk03Jr70M.npz
            └── ...

Citation

@article{lin2023videoxum,
  author    = {Lin, Jingyang and Hua, Hang and Chen, Ming and Li, Yikang and Hsiao, Jenhao and Ho, Chiuman and Luo, Jiebo},
  title     = {VideoXum: Cross-modal Visual and Textural Summarization of Videos},
  journal   = {IEEE Transactions on Multimedia},
  year      = {2023},
}