NTIRE 2024 Challenge on Short-form UGC Video Quality Assessment: Methods and Results

• Main Authors: Li, Xin, Yuan, Kun, Pei, Yajing, Lu, Yiting, Sun, Ming, Zhou, Chao, Chen, Zhibo, Timofte, Radu, Sun, Wei, Wu, Haoning, Zhang, Zicheng, Jia, Jun, Zhang, Zhichao, Cao, Linhan, Chen, Qiubo, Min, Xiongkuo, Lin, Weisi, Zhai, Guangtao, Sun, JianHui, Wang, Tianyi, Li, Lei, Kong, Han, Wang, Wenxuan, Li, Bing, Luo, Cheng, Wang, Haiqiang, Chen, Xiangguang, Meng, Wenhui, Pan, Xiang, Shi, Huiying, Zhu, Han, Xu, Xiaozhong, Sun, Lei, Chen, Zhenzhong, Liu, Shan, Kong, Fangyuan, Fan, Haotian, Xu, Yifang, Xu, Haoran, Yang, Mengduo, Zhou, Jie, Li, Jiaze, Wen, Shijie, Xu, Mai, Li, Da, Yao, Shunyu, Du, Jiazhi, Zuo, Wangmeng, Li, Zhibo, He, Shuai, Ming, Anlong, Fu, Huiyuan, Ma, Huadong, Wu, Yong, Xue, Fie, Zhao, Guozhi, Du, Lina, Guo, Jie, Zhang, Yu, Zheng, Huimin, Chen, Junhao, Liu, Yue, Zhou, Dulan, Xu, Kele, Xu, Qisheng, Sun, Tao, Ding, Zhixiang, Hu, Yuhang
• Format: Conference Proceeding
• Language: English
• Subjects: Benchmark testing; Computer vision; Conferences; Quality assessment; Reviews; Training
• ISSN: 2160-7516
• DOI: 10.1109/CVPRW63382.2024.00643

This paper reviews the NTIRE 2024 Challenge on Short-form UGC Video Quality Assessment (S-UGC VQA), where various excellent solutions are submitted and evaluated on the collected dataset KVQ from popular short-form video platform, i.e., Kuaishou/Kwai Platform. The KVQ database is divided into three parts, including 2926 videos for training, 420 videos for validation, and 854 videos for testing. The purpose is to build new benchmarks and advance the development of S-UGC VQA. The competition had 200 participants and 13 teams submitted valid solutions for the final testing phase. The proposed solutions achieved state-of-the-art performances for S-UGC VQA. The project can be found at https://github.com/lixinustc/KVQ-Challenge-CVPR-NTIRE2024.