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NTIRE 2025 challenge on Text to Image Generation Model Quality Assessment

Fuente: arXiv

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Detalles Bibliográficos
Autores principales:	Han, Shuhao, Fan, Haotian, Kong, Fangyuan, Liao, Wenjie, Guo, Chunle, Li, Chongyi, Timofte, Radu, Li, Liang, Li, Tao, Cui, Junhui, Wang, Yunqiu, Tai, Yang, Sun, Jingwei, Sun, Jianhui, Yue, Xinli, Wang, Tianyi, Hou, Huan, Lu, Junda, Huang, Xinyang, Zhou, Zitang, Zhang, Zijian, Zheng, Xuhui, Wu, Xuecheng, Peng, Chong, Cao, Xuezhi, Nguyen-Mau, Trong-Hieu, Le, Minh-Hoang, Le-Phan, Minh-Khoa, Ly, Duy-Nam, Nguyen, Hai-Dang, Tran, Minh-Triet, Lin, Yukang, Hong, Yan, Song, Chuanbiao, Li, Siyuan, Lan, Jun, Zhang, Zhichao, Li, Xinyue, Sun, Wei, Zhang, Zicheng, Li, Yunhao, Liu, Xiaohong, Zhai, Guangtao, Xu, Zitong, Duan, Huiyu, Wang, Jiarui, Ma, Guangji, Yang, Liu, Liu, Lu, Hu, Qiang, Min, Xiongkuo, Wang, Zichuan, Tang, Zhenchen, Peng, Bo, Dong, Jing, Guan, Fengbin, Yu, Zihao, Lu, Yiting, Luo, Wei, Li, Xin, Lin, Minhao, Chen, Haofeng, He, Xuanxuan, Xu, Kele, Xu, Qisheng, Gao, Zijian, Wan, Tianjiao, Qiu, Bo-Cheng, Hsu, Chih-Chung, Lee, Chia-ming, Lin, Yu-Fan, Yu, Bo, Wang, Zehao, Mu, Da, Chen, Mingxiu, Fang, Junkang, Sun, Huamei, Zhao, Wending, Wang, Zhiyu, Liu, Wang, Yu, Weikang, Duan, Puhong, Sun, Bin, Kang, Xudong, Li, Shutao, He, Shuai, Fu, Lingzhi, Cong, Heng, Zhang, Rongyu, He, Jiarong, Qiao, Zhishan, Huang, Yongqing, Chen, Zewen, Pang, Zhe, Wang, Juan, Guo, Jian, Shao, Zhizhuo, Feng, Ziyu, Li, Bing, Hu, Weiming, Li, Hesong, Liu, Dehua, Liu, Zeming, Xie, Qingsong, Wang, Ruichen, Li, Zhihao, Liang, Yuqi, Bi, Jianqi, Luo, Jun, Yang, Junfeng, Li, Can, Fu, Jing, Xu, Hongwei, Long, Mingrui, Tang, Lulin
Formato:	Preprint
Publicado:	2025
Materias:	Computer Vision and Pattern Recognition Artificial Intelligence
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

_version_	1866912387103195136
author	Han, Shuhao Fan, Haotian Kong, Fangyuan Liao, Wenjie Guo, Chunle Li, Chongyi Timofte, Radu Li, Liang Li, Tao Cui, Junhui Wang, Yunqiu Tai, Yang Sun, Jingwei Sun, Jianhui Yue, Xinli Wang, Tianyi Hou, Huan Lu, Junda Huang, Xinyang Zhou, Zitang Zhang, Zijian Zheng, Xuhui Wu, Xuecheng Peng, Chong Cao, Xuezhi Nguyen-Mau, Trong-Hieu Le, Minh-Hoang Le-Phan, Minh-Khoa Ly, Duy-Nam Nguyen, Hai-Dang Tran, Minh-Triet Lin, Yukang Hong, Yan Song, Chuanbiao Li, Siyuan Lan, Jun Zhang, Zhichao Li, Xinyue Sun, Wei Zhang, Zicheng Li, Yunhao Liu, Xiaohong Zhai, Guangtao Xu, Zitong Duan, Huiyu Wang, Jiarui Ma, Guangji Yang, Liu Liu, Lu Hu, Qiang Min, Xiongkuo Wang, Zichuan Tang, Zhenchen Peng, Bo Dong, Jing Guan, Fengbin Yu, Zihao Lu, Yiting Luo, Wei Li, Xin Lin, Minhao Chen, Haofeng He, Xuanxuan Xu, Kele Xu, Qisheng Gao, Zijian Wan, Tianjiao Qiu, Bo-Cheng Hsu, Chih-Chung Lee, Chia-ming Lin, Yu-Fan Yu, Bo Wang, Zehao Mu, Da Chen, Mingxiu Fang, Junkang Sun, Huamei Zhao, Wending Wang, Zhiyu Liu, Wang Yu, Weikang Duan, Puhong Sun, Bin Kang, Xudong Li, Shutao He, Shuai Fu, Lingzhi Cong, Heng Zhang, Rongyu He, Jiarong Qiao, Zhishan Huang, Yongqing Chen, Zewen Pang, Zhe Wang, Juan Guo, Jian Shao, Zhizhuo Feng, Ziyu Li, Bing Hu, Weiming Li, Hesong Liu, Dehua Liu, Zeming Xie, Qingsong Wang, Ruichen Li, Zhihao Liang, Yuqi Bi, Jianqi Luo, Jun Yang, Junfeng Li, Can Fu, Jing Xu, Hongwei Long, Mingrui Tang, Lulin
author_facet	Han, Shuhao Fan, Haotian Kong, Fangyuan Liao, Wenjie Guo, Chunle Li, Chongyi Timofte, Radu Li, Liang Li, Tao Cui, Junhui Wang, Yunqiu Tai, Yang Sun, Jingwei Sun, Jianhui Yue, Xinli Wang, Tianyi Hou, Huan Lu, Junda Huang, Xinyang Zhou, Zitang Zhang, Zijian Zheng, Xuhui Wu, Xuecheng Peng, Chong Cao, Xuezhi Nguyen-Mau, Trong-Hieu Le, Minh-Hoang Le-Phan, Minh-Khoa Ly, Duy-Nam Nguyen, Hai-Dang Tran, Minh-Triet Lin, Yukang Hong, Yan Song, Chuanbiao Li, Siyuan Lan, Jun Zhang, Zhichao Li, Xinyue Sun, Wei Zhang, Zicheng Li, Yunhao Liu, Xiaohong Zhai, Guangtao Xu, Zitong Duan, Huiyu Wang, Jiarui Ma, Guangji Yang, Liu Liu, Lu Hu, Qiang Min, Xiongkuo Wang, Zichuan Tang, Zhenchen Peng, Bo Dong, Jing Guan, Fengbin Yu, Zihao Lu, Yiting Luo, Wei Li, Xin Lin, Minhao Chen, Haofeng He, Xuanxuan Xu, Kele Xu, Qisheng Gao, Zijian Wan, Tianjiao Qiu, Bo-Cheng Hsu, Chih-Chung Lee, Chia-ming Lin, Yu-Fan Yu, Bo Wang, Zehao Mu, Da Chen, Mingxiu Fang, Junkang Sun, Huamei Zhao, Wending Wang, Zhiyu Liu, Wang Yu, Weikang Duan, Puhong Sun, Bin Kang, Xudong Li, Shutao He, Shuai Fu, Lingzhi Cong, Heng Zhang, Rongyu He, Jiarong Qiao, Zhishan Huang, Yongqing Chen, Zewen Pang, Zhe Wang, Juan Guo, Jian Shao, Zhizhuo Feng, Ziyu Li, Bing Hu, Weiming Li, Hesong Liu, Dehua Liu, Zeming Xie, Qingsong Wang, Ruichen Li, Zhihao Liang, Yuqi Bi, Jianqi Luo, Jun Yang, Junfeng Li, Can Fu, Jing Xu, Hongwei Long, Mingrui Tang, Lulin
contents	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 T2I model quality assessment.
format	Preprint
id	arxiv_https___arxiv_org_abs_2505_16314
institution	arXiv
publishDate	2025
record_format	arxiv
spellingShingle	NTIRE 2025 challenge on Text to Image Generation Model Quality Assessment Han, Shuhao Fan, Haotian Kong, Fangyuan Liao, Wenjie Guo, Chunle Li, Chongyi Timofte, Radu Li, Liang Li, Tao Cui, Junhui Wang, Yunqiu Tai, Yang Sun, Jingwei Sun, Jianhui Yue, Xinli Wang, Tianyi Hou, Huan Lu, Junda Huang, Xinyang Zhou, Zitang Zhang, Zijian Zheng, Xuhui Wu, Xuecheng Peng, Chong Cao, Xuezhi Nguyen-Mau, Trong-Hieu Le, Minh-Hoang Le-Phan, Minh-Khoa Ly, Duy-Nam Nguyen, Hai-Dang Tran, Minh-Triet Lin, Yukang Hong, Yan Song, Chuanbiao Li, Siyuan Lan, Jun Zhang, Zhichao Li, Xinyue Sun, Wei Zhang, Zicheng Li, Yunhao Liu, Xiaohong Zhai, Guangtao Xu, Zitong Duan, Huiyu Wang, Jiarui Ma, Guangji Yang, Liu Liu, Lu Hu, Qiang Min, Xiongkuo Wang, Zichuan Tang, Zhenchen Peng, Bo Dong, Jing Guan, Fengbin Yu, Zihao Lu, Yiting Luo, Wei Li, Xin Lin, Minhao Chen, Haofeng He, Xuanxuan Xu, Kele Xu, Qisheng Gao, Zijian Wan, Tianjiao Qiu, Bo-Cheng Hsu, Chih-Chung Lee, Chia-ming Lin, Yu-Fan Yu, Bo Wang, Zehao Mu, Da Chen, Mingxiu Fang, Junkang Sun, Huamei Zhao, Wending Wang, Zhiyu Liu, Wang Yu, Weikang Duan, Puhong Sun, Bin Kang, Xudong Li, Shutao He, Shuai Fu, Lingzhi Cong, Heng Zhang, Rongyu He, Jiarong Qiao, Zhishan Huang, Yongqing Chen, Zewen Pang, Zhe Wang, Juan Guo, Jian Shao, Zhizhuo Feng, Ziyu Li, Bing Hu, Weiming Li, Hesong Liu, Dehua Liu, Zeming Xie, Qingsong Wang, Ruichen Li, Zhihao Liang, Yuqi Bi, Jianqi Luo, Jun Yang, Junfeng Li, Can Fu, Jing Xu, Hongwei Long, Mingrui Tang, Lulin Computer Vision and Pattern Recognition Artificial Intelligence 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 T2I model quality assessment.
title	NTIRE 2025 challenge on Text to Image Generation Model Quality Assessment
topic	Computer Vision and Pattern Recognition Artificial Intelligence
url	https://arxiv.org/abs/2505.16314