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+ ---
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+ license: cc-by-nc-4.0
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+ ---
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+
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+ # PE Video Dataset (PVD)
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+
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+ [\[📃 Tech Report\]](https://arxiv.org/abs/2504.13181)
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+ [\[📂 Github\]](https://github.com/facebookresearch/perception_models/)
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+
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+ The PE Video Dataset (PVD) is a large-scale collection of 1 million diverse videos, featuring 120,000+ expertly annotated clips. The dataset was introduced in our paper "Perception Encoder".
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+
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+ ## Overview
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+ PE Video Dataset (PVD) comprises 1M high quality and diverse videos. Among them, 120K videos are accompanied by automated and human-verified annotations. and all videos are accompanied with video description and keywords. The videos are motion-centered, covering both first-person and third-person views with a wide coverage of scenes.
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+
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+ ## PVD
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+
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+ ### Key Application
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+
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+ Computer Vision, Video Understanding
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+
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+ ### Intended Use Cases
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+
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+ Train and evaluate video retrieval models
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+ Train and evaluate video captioning models
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+ Primary Data type
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+ Videos
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+ Video caption (Human annotated / Model generated)
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+
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+ ### Data Function
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+ Training, Testing
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+
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+ ### Dataset Characteristics
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+
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+ - Total number of videos: 998,862
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+ - Total number of human annotated captions: 118,862
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+ - Average FPS: 29.8
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+ - Average Video Length: 16.7s
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+ - Average video height: 346
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+ - Average video width: 604
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+
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+ ### Labels
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+ A text description that summarizes the content of a video describing what's happening in the video, such as the actions, events, or objects shown.
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+
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+ ### Nature Of Content
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+
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+ We selected videos from 10 different categories, including hand actions, object interactions, food preparation, work activities, outdoor scenes, animals, water scenes, object handling, close-up shots, and nature scenes.
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+
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+
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+
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+ ### License
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+ CC BY NC 4.0
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+
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+ ### Access Cost
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+ Open access
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+
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+ ### Labeling Methods
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+
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+ The video captions are refined based on the following criteria. The annotators should remove any hallucinations found in the model-generated caption, correct words that describe the video inaccurately, and eliminate repeating or redundant words to make the caption concise and accurate. Additionally, if major actions are missing from the caption, annotators should add them in a concise and natural way.
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+
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+ ### Validation Methods
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+ All of the 118,862 human captions were reviewed by human annotators.
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+
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+ ### Citation
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+ If you find this dataset useful, please cite our papers:
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+ ```
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+ @article{bolya2025perception-encoder,
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+ title={Perception Encoder: The best visual embeddings are not at the output of the network},
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+ author={Daniel Bolya and Po-Yao Huang and Peize Sun and Jang Hyun Cho and Andrea Madotto and Chen Wei and Tengyu Ma and Jiale Zhi and Jathushan Rajasegaran and Hanoona Rasheed and Junke Wang and Marco Monteiro and Hu Xu and Shiyu Dong and Nikhila Ravi and Daniel Li and Piotr Doll{\'a}r and Christoph Feichtenhofer},
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+ journal={arXiv},
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+ year={2025}
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+ }
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+
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+ @article{cho2025perceptionlm,
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+ title={PerceptionLM: Open-Access Data and Models for Detailed Visual Understanding},
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+ author={Jang Hyun Cho and Andrea Madotto and Effrosyni Mavroudi and Triantafyllos Afouras and Tushar Nagarajan and Muhammad Maaz and Yale Song and Tengyu Ma and Shuming Hu and Hanoona Rasheed and Peize Sun and Po-Yao Huang and Daniel Bolya and Suyog Jain and Miguel Martin and Huiyu Wang and Nikhila Ravi and Shashank Jain and Temmy Stark and Shane Moon and Babak Damavandi and Vivian Lee and Andrew Westbury and Salman Khan and Philipp Kr\"{a}henb\"{u}hl and Piotr Doll{\'a}r and Lorenzo Torresani and Kristen Grauman and Christoph Feichtenhofer},
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+ journal={arXiv},
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+ year={2025}
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+ }
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+ ```
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