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[bibtex]@InProceedings{Liu_2024_CVPR, author = {Liu, Xiaohong and Min, Xiongkuo and Zhai, Guangtao and Li, Chunyi and Kou, Tengchuan and Sun, Wei and Wu, Haoning and Gao, Yixuan and Cao, Yuqin and Zhang, Zicheng and Wu, Xiele and Timofte, Radu and Peng, Fei and Fu, Huiyuan and Ming, Anlong and Wang, Chuanming and Ma, Huadong and He, Shuai and Dou, Zifei and Chen, Shu and Zhang, Huacong and Xie, Haiyi and Wang, Chengwei and Chen, Baoying and Zeng, Jishen and Yang, Jianquan and Wang, Weigang and Fang, Xi and Lv, Xiaoxin and Yan, Jun and Zhi, Tianwu and Zhang, Yabin and Li, Yaohui and Li, Yang and Xu, Jingwen and Liu, Jianzhao and Liao, Yiting and Li, Junlin and Yu, Zihao and Guan, Fengbin and Lu, Yiting and Li, Xin and Motamednia, Hossein and Hosseini-Benvidi, S. Farhad and Mahmoudi-Aznaveh, Ahmad and Mansouri, Azadeh and Gankhuyag, Ganzorig and Yoon, Kihwan and Xu, Yifang and Fan, Haotian and Kong, Fangyuan and Zhao, Shiling and Dong, Weifeng and Yin, Haibing and Zhu, Li and Wang, Zhiling and Huang, Bingchen and Saha, Avinab and Mishra, Sandeep and Gupta, Shashank and Sureddi, Rajesh and Saha, Oindrila and Celona, Luigi and Bianco, Simone and Napoletano, Paolo and Schettini, Raimondo and Yang, Junfeng and Fu, Jing and Zhang, Wei and Cao, Wenzhi and Liu, Limei and Peng, Han and Yuan, Weijun and Li, Zhan and Cheng, Yihang and Deng, Yifan and Li, Haohui and Qu, Bowen and Li, Yao and Luo, Shuqing and Wang, Shunzhou and Gao, Wei and Lu, Zihao and Conde, Marcos V. and Timofte, Radu and Wang, Xinrui and Chen, Zhibo and Liao, Ruling and Ye, Yan and Wang, Qiulin and Li, Bing and Zhou, Zhaokun and Geng, Miao and Chen, Rui and Tao, Xin and Liang, Xiaoyu and Sun, Shangkun and Ma, Xingyuan and Li, Jiaze and Yang, Mengduo and Xu, Haoran and Zhou, Jie and Zhu, Shiding and Yu, Bohan and Chen, Pengfei and Xu, Xinrui and Shen, Jiabin and Duan, Zhichao and Asadi, Erfan and Liu, Jiahe and Yan, Qi and Qu, Youran and Zeng, Xiaohui and Wang, Lele and Liao, Renjie}, title = {NTIRE 2024 Quality Assessment of AI-Generated Content Challenge}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2024}, pages = {6337-6362} }
NTIRE 2024 Quality Assessment of AI-Generated Content Challenge
Abstract
This paper reports on the NTIRE 2024 Quality Assessment of AI-Generated Content Challenge which will be held in conjunction with the New Trends in Image Restoration and Enhancement Workshop (NTIRE) at CVPR 2024. This challenge is to address a major challenge in the field of image and video processing namely Image Quality Assessment (IQA) and Video Quality Assessment (VQA) for AI-Generated Content (AIGC). The challenge is divided into the image track and the video track. The image track uses the AIGIQA-20K which contains 20000 AI-Generated Images (AIGIs) generated by 15 popular generative models. The image track has a total of 318 registered participants. A total of 1646 submissions are received in the development phase and 221 submissions are received in the test phase. Finally 16 participating teams submitted their models and fact sheets. The video track uses the T2VQA-DB which contains 10000 AI-Generated Videos (AIGVs) generated by 9 popular Text-to-Video (T2V) models. A total of 196 participants have registered in the video track. A total of 991 submissions are received in the development phase and 185 submissions are received in the test phase. Finally 12 participating teams submitted their models and fact sheets. Some methods have achieved better results than baseline methods and the winning methods in both tracks have demonstrated superior prediction performance on AIGC.
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