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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.25238 |
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| _version_ | 1866918200042586112 |
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| author | Qiao, Qianqian Zheng, DanDan Bo, Yihang Peng, Bao Huang, Heng Jiang, Longteng Wang, Huaye Chen, Jingdong Zhou, Jun Jin, Xin |
| author_facet | Qiao, Qianqian Zheng, DanDan Bo, Yihang Peng, Bao Huang, Heng Jiang, Longteng Wang, Huaye Chen, Jingdong Zhou, Jun Jin, Xin |
| contents | Video aesthetic assessment, a vital area in multimedia computing, integrates computer vision with human cognition. Its progress is limited by the lack of standardized datasets and robust models, as the temporal dynamics of video and multimodal fusion challenges hinder direct application of image-based methods. This study introduces VADB, the largest video aesthetic database with 10,490 diverse videos annotated by 37 professionals across multiple aesthetic dimensions, including overall and attribute-specific aesthetic scores, rich language comments and objective tags. We propose VADB-Net, a dual-modal pre-training framework with a two-stage training strategy, which outperforms existing video quality assessment models in scoring tasks and supports downstream video aesthetic assessment tasks. The dataset and source code are available at https://github.com/BestiVictory/VADB. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_25238 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | VADB: A Large-Scale Video Aesthetic Database with Professional and Multi-Dimensional Annotations Qiao, Qianqian Zheng, DanDan Bo, Yihang Peng, Bao Huang, Heng Jiang, Longteng Wang, Huaye Chen, Jingdong Zhou, Jun Jin, Xin Computer Vision and Pattern Recognition Video aesthetic assessment, a vital area in multimedia computing, integrates computer vision with human cognition. Its progress is limited by the lack of standardized datasets and robust models, as the temporal dynamics of video and multimodal fusion challenges hinder direct application of image-based methods. This study introduces VADB, the largest video aesthetic database with 10,490 diverse videos annotated by 37 professionals across multiple aesthetic dimensions, including overall and attribute-specific aesthetic scores, rich language comments and objective tags. We propose VADB-Net, a dual-modal pre-training framework with a two-stage training strategy, which outperforms existing video quality assessment models in scoring tasks and supports downstream video aesthetic assessment tasks. The dataset and source code are available at https://github.com/BestiVictory/VADB. |
| title | VADB: A Large-Scale Video Aesthetic Database with Professional and Multi-Dimensional Annotations |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2510.25238 |