{"paper":{"title":"AIS 2024 Challenge on Video Quality Assessment of User-Generated Content: Methods and Results","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.MM"],"primary_cat":"cs.CV","authors_text":"Chen Feng, Chenlong He, Chunyi Li, Guangtao Zhai, Haiqiang Wang, Han Zhu, Haoning Wu, Huiying Shi, Jun Jia, Lei Sun, Marcos V. Conde, Nabajeet Barman, Qi Zheng, Radu Timofte, Ruoxi Zhu, Saman Zadtootaghaj, Shan Liu, Steve G\\\"oring, Weisi Lin, Wei Sun, Weixia Zhang, Wenhui Meng, Xiangguang Chen, Xiang Pan, Xiaohong Liu, Xiaozhong Xu, Xiongkuo Min, Yanwei Jiang, Yingjie Zhou, Yuqin Cao, Zhengzhong Tu, Zhenzhong Chen, Zhichao Zhang, Zicheng Zhang, Zihao Qi, Zijian Chen","submitted_at":"2024-04-24T21:02:14Z","abstract_excerpt":"This paper reviews the AIS 2024 Video Quality Assessment (VQA) Challenge, focused on User-Generated Content (UGC). The aim of this challenge is to gather deep learning-based methods capable of estimating the perceptual quality of UGC videos. The user-generated videos from the YouTube UGC Dataset include diverse content (sports, games, lyrics, anime, etc.), quality and resolutions. The proposed methods must process 30 FHD frames under 1 second. In the challenge, a total of 102 participants registered, and 15 submitted code and models. The performance of the top-5 submissions is reviewed and pro"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2404.16205","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2404.16205/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}