metadata
dataset_info:
features:
- name: vclip_id
dtype: string
- name: question_id
dtype: int64
- name: question
dtype: string
- name: answer
dtype: string
- name: frame_indexes
sequence: int64
- name: choices
struct:
- name: A
dtype: string
- name: B
dtype: string
- name: C
dtype: string
- name: D
dtype: string
- name: E
dtype: string
- name: video_metadata
struct:
- name: CLIP-reference-interval-clip
sequence: float64
- name: CLIP-reference-interval-video
sequence: float64
- name: bitrate
dtype: int64
- name: codec
dtype: string
- name: frame_dimensions
sequence: int64
- name: frame_dimensions_resized
sequence: int64
- name: frame_rate
dtype: float64
- name: resolution
dtype: string
- name: resolution_resized
dtype: string
- name: vclip_duration
dtype: float64
- name: vclip_frame_count
dtype: int64
- name: vclip_interval_in_video
sequence: float64
- name: video_duration
dtype: float64
- name: video_frame_count
dtype: int64
- name: video_id
dtype: string
splits:
- name: train
num_bytes: 5358616
num_examples: 11218
- name: test
num_bytes: 1977870
num_examples: 3874
download_size: 2168577
dataset_size: 7336486
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
LV-Haystack: Temporal Search for Long-Form Video Understanding
Jinhui Ye1,
Zihan Wang2,
Haosen Sun2,
Keshigeyan Chandrasegaran1,
Zane Durante1,
Cristobal Eyzaguirre1,
Yonatan Bisk3,
Juan Carlos Niebles1,
Ehsan Adeli1,
Li Fei-Fei1,
Jiajun Wu1,
Manling Li2
Stanford University1, Northwestern University2, Carnegie Mellon University3
Dataset is part of the T* project
🌎Website |
🧑💻Code |
📄arXiv |
🏆 Leaderboard (Coming Soon)
Dataset Sample
{
'vclip_id': '6338b73e-393f-4d37-b278-68703b45908c',
'question_id': 10,
'question': 'What nail did I pull out?',
'answer': 'E',
'frame_indexes': [5036, 5232], # the keyframe indexes
'choices': {
'A': 'The nail from the front wheel fender',
'B': 'The nail from the motorcycle battery compartment',
'C': 'The nail from the left side of the motorcycle seat',
'D': 'The nail from the rearview mirror mount',
'E': 'The nail on the right side of the motorcycle exhaust pipe'
},
'video_metadata': {
'CLIP-reference-interval-vclip': [180.0, 240.0], # Time interval of the "vclip" that is considered to be important by CLIP. this is calculated by (CLIP-reference-interval-video - vclip-interval-in-video[0])
'CLIP-reference-interval-video': [180.0, 240.0], # Time interval of the "video" that is considered to be important by CLIP. This is originally from the **Ego4D dataset**, used in our work for annotators to quickly locate in the video.
'vclip_interval_in_video': [0.0, 480.06667277018227], # the vclip start and end second, i.e., for [a, b], the vclip starts at the a second of the video, ends at the b second of the video
'frame_count': 14155, # Total number of frames in the video
'frame_rate': 30.0, # Frame rate of the video
'duration': 471.8333435058594, # Duration of the video in seconds
'resolution': '454x256', # Original resolution of the video
'frame_dimensions': None, # Frame dimensions (if available)
'codec': 'N/A', # Codec used for the video (if available)
'bitrate': 0, # Bitrate of the video (if available)
'frame_dimensions_resized': [340, 256], # Resized frame dimensions
'resolution_resized': '340x256', # Resized resolution
'video_id': 'b6ae365a-dd70-42c4-90d6-e0351778d991' # Unique video identifier
}
}
Dataset exploration
add hyperlink to demo
Dataset Usage
from datasets import load_dataset
dataset = load_dataset("LVHaystack/LongVideoHaystack")
print(dataset)
>>> DatasetDict({
train: Dataset({
features: ['vclip_id', 'question_id', 'question', 'answer', 'frame_indexes', 'choices', 'video_metadata'],
num_rows: 11218
})
test: Dataset({
features: ['vclip_id', 'question_id', 'question', 'answer', 'frame_indexes', 'choices', 'video_metadata'],
num_rows: 3874
})
})
Video Source Download
TODO: We plan to provide a script of how to download a subset from Ego4d. For now, you can refer to their official guide here. Your code would be look like the follows:
pip install ego4d
ego4d --output_directory=your_path/videos/ \
--datasets full_scale annotations \
--metadata \
--video_uid_file video_uids.txt
python process_videos_to_clips.py
Please find video_uid.txt in our repo, or you can generate it by:
import datasets
metadata = datasets.load_dataset("LVHaystack/LongVideoHaystack-metadata")
with open("video_uids.txt", "w") as file:
for video_id in metadata['video_id']:
file.write(video_id + " ")
then, you need to transform them to video clips:
Dataset Statistics Summary
| Metric | Total | Train | Test |
|---|---|---|---|
| Video Statistics | |||
| Total Videos | 988 | 744 | 244 |
| Total Video Duration (hr) | 423.3 | 322.2 | 101.0 |
| Avg. Video Duration (min) | 25.7 | 26.0 | 24.8 |
| Clip Statistics | |||
| Total Video Clips | 1,324 | 996 | 328 |
| Total Video Clip Duration (hr) | 180.4 | 135.3 | 45.0 |
| Avg. Video Clip Duration (sec) | 8.2 | 8.2 | 8.2 |
| Frame Statistics | |||
| Total Frames (k) | 45,700 | 34,800 | 10,900 |
| Avg. Frames per Video (k) | 46.3 | 46.8 | 44.7 |
| Ratio of Keyframe / Frame (‰) | 0.62 | 0.59 | 0.71 |
| QA Statistics | |||
| Total QA Pairs | 15,092 | 11,218 | 3,874 |
| Avg. QA Pair per Video | 15.3 | 15.1 | 15.9 |
| Avg. QA Pair per Clip | 11.4 | 11.3 | 11.8 |
| Avg. Keyframes per Question | 1.88 | 1.84 | 2.01 |
Evaluation scripts
Please refer to ./eval.py.
Contact
- Jinhui Ye: [email protected]
- Zihan Wang: [email protected] (datasets)
- Haosen Sun: [email protected]
- Keshigeyan Chandrasegaran: [email protected]
- Manling Li: [email protected]
Citation
@misc{tstar,
title={Re-thinking Temporal Search for Long-Form Video Understanding},
author={Jinhui Ye and Zihan Wang and Haosen Sun and Keshigeyan Chandrasegaran and Zane Durante and Cristobal Eyzaguirre and Yonatan Bisk and Juan Carlos Niebles and Ehsan Adeli and Li Fei-Fei and Jiajun Wu and Manling Li},
year={2025},
eprint={2501.TODO},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
Website template borrowed from HourVideo.