USV: Towards Understanding the User-generated Short-form Videos
The paper titled 'USV: Towards Understanding the User-generated Short-form Videos' introduces a new dataset aimed at enhancing video understanding. This dataset, consisting of approximately 224,000 user-generated short-form videos, focuses on high-level semantic information rather than just instance-level recognition. The authors propose two baseline methods to address topic recognition and video-text retrieval tasks, facilitating future research in this area.
- ▪The USV dataset includes around 224,000 videos collected from user-generated content platforms.
- ▪The focus of the research is on high-level semantic video understanding rather than instance-level recognition.
- ▪Two baseline methods, Multi-Modality Fusion Network and Video-Text Contrastive Learning, are proposed for specific tasks.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.20838 |
| Publication time | Fri, 22 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-22T04:02:00.009Z |
| Last seen | 2026-05-22T04:02:00.009Z |
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| Commercial reuse | May the content be reused commercially? | Not permitted |
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Computer Science > Computer Vision and Pattern Recognition arXiv:2605.20838 (cs) [Submitted on 20 May 2026] Title:USV: Towards Understanding the User-generated Short-form Videos Authors:Haoyue Cheng, Su Xu, Liwei Jin, Wayne Wu, Chen Qian, Limin Wang View a PDF of the paper titled USV: Towards Understanding the User-generated Short-form Videos, by Haoyue Cheng and 5 other authors View PDF HTML (experimental) Abstract:Several large-scale video datasets have been published these years and have advanced the area of video understanding. However, the newly emerged user-generated short-form videos have rarely been studied. This paper presents USV, the User-generated Short-form Video dataset for high-level semantic video understanding.
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