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.

翻译：视频美学评估作为多媒体计算的重要领域，融合了计算机视觉与人类认知。由于视频的时序动态特性与多模态融合的挑战阻碍了基于图像方法的直接应用，该领域的发展受限于标准化数据集的缺乏和鲁棒模型的不足。本研究提出了VADB，这是目前最大的视频美学数据库，包含10,490个多样化视频，由37位专业人员从多个美学维度进行标注，涵盖整体美学评分、属性特定美学评分、丰富的语言评论及客观标签。我们提出了VADB-Net，一种采用两阶段训练策略的双模态预训练框架，在评分任务中超越了现有视频质量评估模型，并支持下游视频美学评估任务。数据集与源代码已公开于https://github.com/BestiVictory/VADB。