--- 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 Ye1Zihan Wang2Haosen Sun2Keshigeyan Chandrasegaran1Zane Durante1
Cristobal Eyzaguirre1Yonatan Bisk3Juan Carlos Niebles1Ehsan Adeli1Li Fei-Fei1Jiajun Wu1Manling Li2
 Stanford University1, Northwestern University2, Carnegie Mellon University3

🌎Website | 🧑‍💻Code | 📄arXiv | 🏆 Leaderboard (Coming Soon)

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Dataset is part of the T* project

#### Dataset Sample ```python { '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 ```python from datasets import load_dataset dataset = load_dataset("LVHaystack/LongVideoHaystack") print(dataset) ``` ```bash >>> 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 }) }) ``` #### 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 | #### Download Videos Assume your video is in ./videos/ #### Evaluation scripts Please refer to ./eval.py (add hyperlink). #### Contact - Jinhui Ye: jinhuiyes@gmail.com - Zihan Wang: zihanw@u.northwestern.edu - Haosen Sun: haosensun2026@u.northwestern.edu - Keshigeyan Chandrasegaran: keshik@stanford.edu - Manling Li: manling.li@northwestern.edu #### Citation ```bibtex @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](https://huggingface.co/datasets/HourVideo/HourVideo).