| --- |
| 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 |
| - [Table of Contents](#table-of-contents) |
| - [Dataset Description](#dataset-description) |
| - [Dataset Summary](#dataset-summary) |
| - [Languages](#languages) |
| - [Dataset Structure](#dataset-structure) |
| - [Data Splits](#data-splits) |
| - [Data Resources](#data-resources) |
| - [Data Fields](#data-fields) |
| - [Annotation Sample](#annotation-sample) |
| - [Citation](#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. |
| ``` json |
| { |
| '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 |
|
|
| ```bibtex |
| @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}, |
| } |
| ``` |
|
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