metadata
dataset_info:
features:
- name: video_id
dtype: string
- name: vclip_id
dtype: string
- name: question
dtype: string
- name: options
struct:
- name: A
dtype: string
- name: B
dtype: string
- name: C
dtype: string
- name: D
dtype: string
- name: E
dtype: string
- name: answer
dtype: string
- name: frame_indexes_video
sequence: int64
- name: frame_indexes_vclip
sequence: int64
- 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: 6862627
num_examples: 12892
- name: val
num_bytes: 589648
num_examples: 1000
- name: test_tiny
num_bytes: 107818
num_examples: 200
- name: test
num_bytes: 528993
num_examples: 1000
download_size: 2374756
dataset_size: 8089086
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: val
path: data/val-*
- split: test_tiny
path: data/test_tiny-*
- 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
CVPR 2025 . Dataset is part of the T* project
🌎Website |
🧑💻Code |
📄arXiv |
🏆 Leaderboard (Coming Soon)
Dataset Sample
{
'video_id': 'b6ae365a-dd70-42c4-90d6-e0351778d991',
'vclip_id': '6338b73e-393f-4d37-b278-68703b45908c',
'question_id': 10,
'question': 'What nail did I pull out?',
'answer': 'E',
'frame_indexes_vclip': [5036, 5232], # the keyframe indexes in the vclip
'frame_indexes_video': [5036, 5232], # the keyframe indexes in the video
'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 that are valid and unbroken, 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['train'])
Dataset({
features: ['video_id', 'vclip_id', 'question', 'options', 'answer', 'frame_indexes_video', 'frame_indexes_vclip', 'video_metadata'],
num_rows: 12892
})
Download and Process Video Source
TODO: We plan to provide a script of how to download a subset from Ego4d and process them. Below is download part adapted from their official guide and we will add video2clip script soon.
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 # TODO
Please find video_uids.txt in our repo, or you can generate it by:
import datasets
metadata = datasets.load_dataset("LVHaystack/LongVideoHaystack-metadata")["metadata"]
with open("video_uids.txt", "w") as file:
for video_id in list(set(metadata['video_id'])):
file.write(video_id + " ")
To follow evaluation for LongVideoBench in our paper, please find script to transform LongVideoBench to LongVideoHaystack format in transform_longvideobench.py.
Dataset Statistics Summary
| Metric | Total | Train | Val | Test | Test_Tiny |
|---|---|---|---|---|---|
| Video Statistics | |||||
| Total Videos | 988 | 858 | 71 | 53 | 13 |
| Total Video Duration (hr) | 423.30 | 371.76 | 27.39 | 24.42 | 4.33 |
| Avg. Video Duration (min) | 25.71 | 26.00 | 23.15 | 27.65 | 19.97 |
| Clip Statistics | |||||
| Total Video Clips | 1,324 | 1,141 | 89 | 80 | 17 |
| Total Video Clip Duration (hr) | 180.40 | 156.09 | 11.97 | 10.56 | 2.18 |
| Avg. Video Clip Duration (sec) | 490.50 | 492.49 | 484.05 | 474.98 | 461.53 |
| Frame Statistics | |||||
| Total Frames (k) | 45,716 | 40,150 | 2,958 | 2,637 | 467 |
| Avg. Frames per Video (k) | 46.27 | 46.80 | 41.66 | 49.76 | 35.95 |
| Ratio of Keyframe / Frame (‰) | 0.62 | 0.59 | 0.69 | 0.78 | 0.89 |
| QA Statistics | |||||
| Total QA Pairs | 15,092 | 12,892 | 1,000 | 1,000 | 200 |
| Avg. QA Pair per Video | 15.28 | 15.03 | 14.08 | 18.87 | 15.38 |
| Avg. QA Pair per Clip | 11.40 | 11.30 | 11.24 | 12.50 | 11.76 |
| Avg. Keyframes per Question | 1.88 | 1.85 | 2.05 | 2.07 | 2.07 |
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.