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 20,000 AI-Generated Images (AIGIs) generated by 15 popular generative models. The image track has a total of 318 registered participants. A total of 1,646 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 10,000 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.
Abstract (translated)
这篇论文报告了NTIRE 2024人工智能生成内容挑战赛，该挑战赛将与CVPR 2024中的图像修复和增强研讨会（NTIRE）同时举办。这项挑战的目标是解决图像和视频处理领域的一个重大挑战，即人工智能生成内容（AIGC）的图像质量和视频质量评估（VQA）。挑战分为图像赛道和视频赛道。图像赛道使用了AIGIQA-20K，它包含了由15个流行生成模型生成的20,000个AI生成图像（AIGIs）。图像赛道共有318名注册参与者。在开发阶段共收到1,646篇提交，测试阶段收到了221篇提交。最后，16支参赛队伍提交了他们的模型和报告。视频赛道使用了T2VQA-DB，它包含了由9个流行文本转视频（T2V）模型生成的10,000个AI生成视频（AIGVs）。共有196名参与者登记注册在视频赛道上。在开发阶段共收到991篇提交，测试阶段收到了185篇提交。最后，12支参赛队伍提交了他们的模型和报告。有些方法取得了比基线方法更好的效果，两赛道获胜的方法在AIGC上表现出卓越的预测性能。
URL
https://arxiv.org/abs/2404.16687
