VADB: A Large-Scale Video Aesthetic Database with Professional and Multi-Dimensional Annotations

Qianqian Qiao, DanDan Zheng, Yihang Bo, Bao Peng, Heng Huang, Longteng Jiang, Huaye Wang, Jingdong Chen, JUN ZHOU, Xin Jin

Keywords: video aesthetic, VADB dataset, aesthetic scoring model, multi-dimensional aesthetic attributes, rich language comments

TL;DR: This study introduces VADB, the largest video aesthetic database with 10,490 videos, and VADB-Net, a novel framework that outperforms existing models in video aesthetic assessment.

Abstract: 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.

Dataset URL: https://huggingface.co/datasets/BestiVictoryLab/VADB

Code URL: https://github.com/BestiVictory/VADB

Primary Area: Datasets & Benchmarks for applications in computer vision

Flagged For Ethics Review: true

Submission Number: 1695