Initial Commit

.gitattributes

@@ -53,3 +53,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.jpg filter=lfs diff=lfs merge=lfs -text
 *.jpeg filter=lfs diff=lfs merge=lfs -text
 *.webp filter=lfs diff=lfs merge=lfs -text
+train_videoxum.json filter=lfs diff=lfs merge=lfs -text
+val_videoxum.json filter=lfs diff=lfs merge=lfs -text
+test_videoxum.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,94 @@
+---
+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)
+}
+```
+
+
+## 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},
+}
+```
+

test_videoxum.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d60d44e81ee815c0e2dc0d3eae8c49cbfcc23af67d1caf44e9dea70cc052a54d
+size 66271547

train_videoxum.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1df2b42a15c02bbe3228178c35fe77fda29f478f07b0a8248a6bdf59c02ba703
+size 38357573

val_videoxum.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2832f423ff5fd768a979665e30345a56f1723aacc1821986b5ec042bed30f556
+size 9575564