VQualA 2025 Challenge on Engagement Prediction for Short Videos: Methods and Results
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.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Engagement Prediction of Short Videos with Large Multimodal Models
The rapid proliferation of user-generated content (UGC) on short-form video platforms has made video engagement prediction increasingly important for optimizing recommendation systems and guiding content creation. Howeve…
Recommendation SystemsDelving Deep into Engagement Prediction of Short Videos
Understanding and modeling the popularity of User Generated Content (UGC) short videos on social media platforms presents a critical challenge with broad implications for content creators and recommendation systems. This…
PredictionRecommendation SystemsVideo Quality AssessmentA Computational Model of Message Sensation Value in Short Video Multimodal Features that Predicts Sensory and Behavioral Engagement
The contemporary media landscape is characterized by sensational short videos. While prior research examines the effects of individual multimodal features, the collective impact of multimodal features on viewer engagemen…
VQualA 2025 Challenge on Face Image Quality Assessment: Methods and Results
Face images play a crucial role in numerous applications; however, real-world conditions frequently introduce degradations such as noise, blur, and compression artifacts, affecting overall image quality and hindering sub…
Face Image Quality AssessmentUnderstanding Virality: A Rubric based Vision-Language Model Framework for Short-Form Edutainment Evaluation
Evaluating short-form video content requires moving beyond surface-level quality metrics toward human-aligned, multimodal reasoning. While existing frameworks like VideoScore-2 assess visual and semantic fidelity, they d…
Multimodal ReasoningFeature Importance