AI 中文总结
针对短视频与AIGC内容兴起，提出TREND-10K数据集，含1万视频，基于趋势与静态采样，从技术、美学、AIGC痕迹三维度统一评估视频质量，确保高质量标注与泛化。
AI 中文摘要
短视频平台的日益突出，加上AI生成内容（AIGC）视频的先进商业化，导致用户日常生活中消费的视频媒体趋势类型发生了转变。传统的用户生成内容（UGC）正逐渐被专业短剧和AIGC娱乐所取代。因此，针对当代媒体内容的视频质量评估（VQA）变得越来越重要。这需要一个统一的评估框架，能够处理多样化的视频内容和不断演变的媒体趋势。在此背景下，我们引入了TREND-10K，一个下一代综合性VQA数据集，由趋势驱动部分和静态部分组成，包含10,000个涵盖广泛内容类型的视频。趋势驱动部分基于TREND-Search框架，该框架从在线平台的趋势列表中捕获用户偏好画像，并根据这些画像制定采样策略。静态部分则由从公开数据集中选取的补充样本组成。为了支持对各种视频类型的统一评估，我们纳入了三个评估维度：技术、美学和AIGC痕迹。实验表明，我们的数据集确保了高标注质量，并在多个内容类别中表现出显著的泛化能力。总之，我们的工作为推进VQA提供了一个稳健的框架，解决了用户感知习惯和偏好随时间演变所带来的挑战。
英文摘要
The increasing prominence of short-video platforms, coupled with the advanced commercialization of AI-generated content (AIGC) videos, has led to a shift in the types of video media trend consumed by users in their daily lives. Traditional user-generated content (UGC) is gradually being replaced by professional short dramas and AIGC entertainment. Consequently, VQA for contemporary media content has become increasingly important. This requires a unified evaluation framework that can handle diverse video content and evolving media trends. In this context, we introduce TREND-10K, a next-generation comprehensive VQA dataset consisting of the trend-driven part and the static part, containing $10,000$ videos across a wide spectrum of content types. The trend-driven part is based on the TREND-Search framework, which captures user preference profiles from trending lists on online platforms and formulates sampling strategies based on these profiles. The static part, on the other hand, is composed of supplementary samples selected from publicly available datasets. To support unified evaluation for various video types, we incorporate three evaluation dimensions: technical, aesthetic, and AIGC-trace. Experiments show that our dataset ensures high annotation quality and exhibits remarkable generalization across multiple content categories. In conclusion, our work presents a robust framework for advancing VQA, addressing challenges caused by the temporal evolution of user perceptual habits and preferences.
