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Datasets:
J5612138
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UrbanVideo-Bench

Tasks:
Visual Question Answering
Modalities:
Text
Video
Formats:
parquet
Languages:
English
Size:
1K - 10K
ArXiv:
Tags:
Video
Text
Embodied
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J5612138 trainandtest666 commited on
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Duplicate from EmbodiedCity/UrbanVideo-Bench

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Co-authored-by: BN Zhao <trainandtest666@users.noreply.huggingface.co>

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+ ---
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+ license: mit
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+ task_categories:
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+ - visual-question-answering
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+ language:
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+ - en
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+ tags:
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+ - Video
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+ - Text
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+ - Embodied
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+ size_categories:
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+ - 1K<n<10K
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+ configs:
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+ - config_name: benchmark
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+ data_files: MCQ.parquet
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+ ---
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+
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+ # [ACL'25 Oral] UrbanVideo-Bench: Benchmarking Vision-Language Models on Embodied Intelligence with Video Data in Urban Spaces
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+ This repository contains the dataset introduced in the paper, consisting of two parts: 5k+ multiple-choice question-answering (MCQ) data and 1k+ video clips.
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+
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+ - **Arxiv**: https://arxiv.org/pdf/2503.06157
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+ - **Project**: https://embodiedcity.github.io/UrbanVideo-Bench/
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+ - **Code**: https://github.com/EmbodiedCity/UrbanVideo-Bench.code
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+
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+ ## Dataset Description
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+ The benchmark is designed to evaluate whether video-large language models (Video-LLMs) can naturally process continuous first-person visual observations like humans, enabling recall, perception, reasoning, and navigation.
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+
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+ - **Text Data**: Available in the file `MCQ.parquet`.
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+ - **Video Data**: Stored in the `videos` folder.
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+
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+ The `MCQ.parquet` contains the following fields:
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+
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+
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+ | Field Name | Description |
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+ |:--------------------|:----------------------------------------|
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+ | `Question_id` | Global index of each MCQ |
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+ | `video_id` | Video name for each MCQ |
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+ | `question_category` | Task category corresponding to each MCQ |
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+ | `question` | Text of question and options |
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+ | `answer` | Ground truth answer for the MCQ |
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+
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+ For example, we evaluate the performance of 17 popular Video-LLMs on the dataset.
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+ For detailed results and analysis, please refer to the paper.
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+ ![UrbanVideo-Bench](evaluations.jpg)
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+
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+ ## Citation
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+
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+ ```bibtex
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+ @misc{zhao2025urbanvideobench,
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+ title={UrbanVideo-Bench: Benchmarking Vision-Language Models on Embodied Intelligence with Video Data in Urban Spaces},
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+ author={Baining Zhao and Jianjie Fang and Zichao Dai and Ziyou Wang and Jirong Zha and Weichen Zhang and Chen Gao and Yue Wang and Jinqiang Cui and Xinlei Chen and Yong Li},
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+ year={2025},
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+ eprint={2503.06157},
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+ archivePrefix={arXiv},
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+ primaryClass={cs.CV},
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+ url={https://arxiv.org/abs/2503.06157},
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+ }
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+ ```
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