VC-Agent: An Interactive Agent for Customized Video Dataset Collection
Fuente:
arXiv
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| Autori principali: | , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
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| _version_ | 1866909806031273984 |
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| author | Zhang, Yidan Xu, Mutian Hao, Yiming Zhou, Kun Chang, Jiahao Liu, Xiaoqiang Wan, Pengfei Fu, Hongbo Han, Xiaoguang |
| author_facet | Zhang, Yidan Xu, Mutian Hao, Yiming Zhou, Kun Chang, Jiahao Liu, Xiaoqiang Wan, Pengfei Fu, Hongbo Han, Xiaoguang |
| contents | Facing scaling laws, video data from the internet becomes increasingly important. However, collecting extensive videos that meet specific needs is extremely labor-intensive and time-consuming. In this work, we study the way to expedite this collection process and propose VC-Agent, the first interactive agent that is able to understand users' queries and feedback, and accordingly retrieve/scale up relevant video clips with minimal user input. Specifically, considering the user interface, our agent defines various user-friendly ways for the user to specify requirements based on textual descriptions and confirmations. As for agent functions, we leverage existing multi-modal large language models to connect the user's requirements with the video content. More importantly, we propose two novel filtering policies that can be updated when user interaction is continually performed. Finally, we provide a new benchmark for personalized video dataset collection, and carefully conduct the user study to verify our agent's usage in various real scenarios. Extensive experiments demonstrate the effectiveness and efficiency of our agent for customized video dataset collection. Project page: https://allenyidan.github.io/vcagent_page/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_21291 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | VC-Agent: An Interactive Agent for Customized Video Dataset Collection Zhang, Yidan Xu, Mutian Hao, Yiming Zhou, Kun Chang, Jiahao Liu, Xiaoqiang Wan, Pengfei Fu, Hongbo Han, Xiaoguang Artificial Intelligence Computer Vision and Pattern Recognition Facing scaling laws, video data from the internet becomes increasingly important. However, collecting extensive videos that meet specific needs is extremely labor-intensive and time-consuming. In this work, we study the way to expedite this collection process and propose VC-Agent, the first interactive agent that is able to understand users' queries and feedback, and accordingly retrieve/scale up relevant video clips with minimal user input. Specifically, considering the user interface, our agent defines various user-friendly ways for the user to specify requirements based on textual descriptions and confirmations. As for agent functions, we leverage existing multi-modal large language models to connect the user's requirements with the video content. More importantly, we propose two novel filtering policies that can be updated when user interaction is continually performed. Finally, we provide a new benchmark for personalized video dataset collection, and carefully conduct the user study to verify our agent's usage in various real scenarios. Extensive experiments demonstrate the effectiveness and efficiency of our agent for customized video dataset collection. Project page: https://allenyidan.github.io/vcagent_page/. |
| title | VC-Agent: An Interactive Agent for Customized Video Dataset Collection |
| topic | Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2509.21291 |