NTIRE 2025 challenge on Text to Image Generation Model Quality Assessment

• URL: http://arxiv.org/abs/2505.16314v1
• Date: Thu, 22 May 2025 07:12:36 GMT
• Title: NTIRE 2025 challenge on Text to Image Generation Model Quality Assessment
• Abstract summary: This paper reports on the NTIRE 2025 challenge on Text to Image (T2I) generation model quality assessment.<n>The aim of this challenge is to address the fine-grained quality assessment of text-to-image generation models.
• Score: 146.76913448156176
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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