基于真实用户互动数据，预测短视频受欢迎程度。
VQualA 2025 Challenge on Engagement Prediction for Short Videos: Methods and Results
- 利用视觉、音频和元数据等多模态特征建模
- 97人参与，15份有效提交，提升预测性能
- 适合对短视频推荐与用户行为分析感兴趣者
本文概述了与ICCV 2025联合举办的VQualA 2025短视频吸引力预测挑战赛。挑战聚焦于理解并建模社交媒体平台上用户生成内容（UGC）短视频的流行度。为支持该目标，挑战赛引入了一个新发布的短格式UGC数据集，其包含来自真实用户交互的互动指标。挑战旨在推动鲁棒建模策略的发展，以捕捉影响用户参与度的复杂因素。参赛者探索了包括视觉内容、音频信息及创作者提供的元数据在内的多种多模态特征。挑战吸引了97名参与者，共收到15份有效测试提交，显著推动了短格式UGC视频吸引力预测领域的进展。
原文摘要 · Abstract (English)
This paper presents an overview of the VQualA 2025 Challenge on Engagement Prediction for Short Videos, held in conjunction with ICCV 2025. The challenge focuses on understanding and modeling the popularity of user-generated content (UGC) short videos on social media platforms. To support this goal, the challenge uses a new short-form UGC dataset featuring engagement metrics derived from real-world user interactions. This objective of the Challenge is to promote robust modeling strategies that capture the complex factors influencing user engagement. Participants explored a variety of multi-modal features, including visual content, audio, and metadata provided by creators. The challenge attracted 97 participants and received 15 valid test submissions, contributing significantly to progress in short-form UGC video engagement prediction.
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