metadata

dataset_info:
  features:
    - name: vclip_id
      dtype: string
    - name: question_id
      dtype: int32
    - name: question
      dtype: string
    - name: answer
      dtype: string
    - name: frame_indexes
      sequence: int32
    - 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
          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: video_duration
          dtype: float64
        - name: video_frame_count
          dtype: int64
        - name: video_id
          dtype: string
  splits:
    - name: train
      num_bytes: 4782472
      num_examples: 11218
    - name: test
      num_bytes: 1776278
      num_examples: 3874
  download_size: 1999818
  dataset_size: 6558750
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': [180.0, 240.0],  # Time interval of the video that is considered to be important in CLIP. This is originally from the Ego4D dataset, used here for annotators to quickly locate in 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. Assume your video will be downloaded to your_path/videos/ .

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

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.