NTIRE 2024 Challenge on Short-form UGC Video Quality Assessment: Methods and Results
Xin Li
Kun Yuan
Yajing Pei
Yiting Lu
Ming-hui Sun
Chao Zhou
Zhibo Chen
Radu Timofte
Wei Sun
Haoning Wu
Zicheng Zhang
Jun Jia
Zhichao Zhang
Lin-Li Cao
Qiubo Chen
Xiongkuo Min
Weisi Lin
Guangtao Zhai
Jianhui Sun
Tianyi Wang
Lei Li
Han Kong
Wenxuan Wang
Bing Li
Cheng Luo
Haiqiang Wang
Xiang-Zhong Chen
Wenhui Meng
Xiang Pan
Huiying Shi
Han Zhu
Xiaozhong Xu
Lei Sun
Zhenzhong Chen
Sha Liu
Fan-zhi Kong
Hao Fan
Y. Xu
Haoran Xu
Meng-Zhao Yang
Jie Zhou
Jiaze Li
Shijie Wen
Mai Xu
Da Li
Shunyu Yao
Jiazhi Du
Wangmeng Zuo
Zhibo Li
Shuaiqi He
Anlong Ming
Hui Fu
Hua-Min Ma
Yong Wu
Fie Xue
Guozhi Zhao
Li-Fen Du
Jie Guo
Yu Zhang
Hu Zheng
Junhao Chen
Yue Liu
Dulan Zhou
Kele Xu
Qisheng Xu
Tao Sun
Zhi-Guo Ding
Yuhan Hu

Abstract
This paper reviews the NTIRE 2024 Challenge on Shortform 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/KVQChallenge-CVPR-NTIRE2024.
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