TIRE 2025 challenge on Text to Image Generation Model Quality Assessment

作者：Shuhao Han, Haotian Fan, Fangyuan Kong, Wenjie Liao, Chunle Guo, Chongyi Li, Radu Timofte, Liang Li, Tao Li, Junhui Cui, Yunqiu Wang, Yang Tai, Jingwei Sun, Jianhui Sun, Xu Yue, Tianyi Wang, Huan Hou, Junda Lu, Xinyang Huang, Zitang Zhou, Zijian Zhang, Xuhui Zheng, Xuecheng Wu, Chong Peng, Xuezhi Cao, Trong-Hieu Nguyen-Mau, M. Le, Minh-Khoa Le-Phan, Duy-Nam Ly, Hai-Dang Nguyen, Minh–Triet Tran, Yukang Lin, Hong Yan, Chuanbiao Song, Siyuan Li, Jun Lan, Zhichao Zhang, Xinyue Li, Wei Sun, Zicheng Zhang, Yunhao Li, Xiaohong Liu, Guangtao Zhai, Zitong Xu, Huiyu Duan, Jiarui Wang, Guang Ma, Yang Liu, Lu Liu, Qiang Hu, Xiongkuo Min, Zichuan Wang, Zhenchen Tang, Bo Peng, Jing Dong, Fengbin Guan, Zihao Yu, Yiting Lu, Wei Luo, Xin Li, Minhao Lin, Haofeng Chen, Xuanxuan He, Kele Xu, Qisheng Xu, Zijian Gao, Tianjiao Wan, Bo-Cheng Qiu, Chih–Chung Hsu, Chia-Ming Lee, Yu-Fan Lin, Bo Yu, Zehao Wang, Da Mu, Mingxiu Chen, Junkang Fang, Huamei Sun, Wending Zhao, Zhiyu Wang, Wang Liu, Weikang Yu, Puhong Duan, Bin Sun, Xudong Kang, Shutao Li, Shuai He, Lingzhi Fu, Heng Cong, Rongyu Zhang, Jiarong He, Zhishan Qiao, Yongqing Huang, Zewen Chen, Zhe Pang, Juan Wang, Jian Guo, Zhutao Shao, Ziyu Feng, Bing Li, Weiming Hu, Hesong Li, Dehua Liu, Zeming Liu, Qingsong Xie, Ruichen Wang, Zhihao Li, Yuqi Liang, Jianqi Bi, Jun Luo, Junfeng Yang, Can Li, Jing Fu, Hongwei Xu, Mingrui Long, Tang Li · 年份：2025 · DOI：10.1109/cvprw67362.2025.00107 · 被引用次数：1 · 研究领域：Scientific Computing and Data Management、Advanced Data Processing Techniques、Radiomics and Machine Learning in Medical Imaging

This paper reports on the NTIRE 2025 challenge on Text to Image (T2I) generation model quality assessment, which will be held in conjunction with the New Trends in Image Restoration and Enhancement Workshop (NTIRE) at CVPR 2025. The aim of this challenge is to address the fine-grained quality assessment of text-to-image generation models. This challenge evaluates text-to-image models from two aspects: image-text alignment and image structural distortion detection, and is divided into the alignment track and the structural track. The alignment track uses the EvalMuse-40K, which contains around 40K AI-Generated Images (AIGIs) generated by 20 popular generative models. The alignment track has a total of 371 registered participants. A total of 1,883 submissions are received in the development phase, and 507 submissions are received in the test phase. Finally, 12 participating teams submitted their models and fact sheets. The structure track uses the EvalMuse-Structure, which contains 10,000 AI-Generated Images (AIGIs) with corresponding structural distortion mask. A total of 211 participants have registered in the structure track. A total of 1155 submissions are received in the development phase, and 487 submissions are received in the test phase. Finally, 8 participating teams submitted their models and fact sheets. Almost all methods have achieved better results than baseline methods, and the winning methods in both tracks have demonstrated superior prediction performance on T2...