VQualA 2025 Challenge on Engagement Prediction for Short Videos: Methods and Results
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| Format: | Preprint |
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2025
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| _version_ | 1866912568908447744 |
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| author | Li, Dasong Ma, Sizhuo Hua, Hang Li, Wenjie Wang, Jian Zhou, Chris Wei Guan, Fengbin Li, Xin Yu, Zihao Lu, Yiting Liao, Ru-Ling Ye, Yan Chen, Zhibo Sun, Wei Cao, Linhan Cao, Yuqin Zhang, Weixia Wen, Wen Zhang, Kaiwei Chen, Zijian Lu, Fangfang Min, Xiongkuo Zhai, Guangtao Xiao, Erjia Zhang, Lingfeng Su, Zhenjie Cheng, Hao Liu, Yu Xu, Renjing Chen, Long Hao, Xiaoshuai Zeng, Zhenpeng Wu, Jianqin Wang, Xuxu Yu, Qian Hu, Bo Wang, Weiwei Liu, Pinxin Tang, Yunlong Song, Luchuan He, Jinxi Wu, Jiaru Lyu, Hanjia |
| author_facet | Li, Dasong Ma, Sizhuo Hua, Hang Li, Wenjie Wang, Jian Zhou, Chris Wei Guan, Fengbin Li, Xin Yu, Zihao Lu, Yiting Liao, Ru-Ling Ye, Yan Chen, Zhibo Sun, Wei Cao, Linhan Cao, Yuqin Zhang, Weixia Wen, Wen Zhang, Kaiwei Chen, Zijian Lu, Fangfang Min, Xiongkuo Zhai, Guangtao Xiao, Erjia Zhang, Lingfeng Su, Zhenjie Cheng, Hao Liu, Yu Xu, Renjing Chen, Long Hao, Xiaoshuai Zeng, Zhenpeng Wu, Jianqin Wang, Xuxu Yu, Qian Hu, Bo Wang, Weiwei Liu, Pinxin Tang, Yunlong Song, Luchuan He, Jinxi Wu, Jiaru Lyu, Hanjia |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_02969 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | VQualA 2025 Challenge on Engagement Prediction for Short Videos: Methods and Results Li, Dasong Ma, Sizhuo Hua, Hang Li, Wenjie Wang, Jian Zhou, Chris Wei Guan, Fengbin Li, Xin Yu, Zihao Lu, Yiting Liao, Ru-Ling Ye, Yan Chen, Zhibo Sun, Wei Cao, Linhan Cao, Yuqin Zhang, Weixia Wen, Wen Zhang, Kaiwei Chen, Zijian Lu, Fangfang Min, Xiongkuo Zhai, Guangtao Xiao, Erjia Zhang, Lingfeng Su, Zhenjie Cheng, Hao Liu, Yu Xu, Renjing Chen, Long Hao, Xiaoshuai Zeng, Zhenpeng Wu, Jianqin Wang, Xuxu Yu, Qian Hu, Bo Wang, Weiwei Liu, Pinxin Tang, Yunlong Song, Luchuan He, Jinxi Wu, Jiaru Lyu, Hanjia Computer Vision and Pattern Recognition Multimedia Social and Information Networks 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. |
| title | VQualA 2025 Challenge on Engagement Prediction for Short Videos: Methods and Results |
| topic | Computer Vision and Pattern Recognition Multimedia Social and Information Networks |
| url | https://arxiv.org/abs/2509.02969 |