arXiv:2307.00211 공식 코드 · Papers with Code

AIGCIQA2023: A Large-scale Image Quality Assessment Database for AI Generated Images: from the Perspectives of Quality, Authenticity and Correspondence

Jiarui Wang, Huiyu Duan, Jing Liu, Shi Chen, Xiongkuo Min, Guangtao Zhai

제출 2023-07-01

원문 초록

In this paper, in order to get a better understanding of the human visual preferences for AIGIs, a large-scale IQA database for AIGC is established, which is named as AIGCIQA2023. We first generate over 2000 images based on 6 state-of-the-art text-to-image generation models using 100 prompts. Based on these images, a well-organized subjective experiment is conducted to assess the human visual preferences for each image from three perspectives including quality, authenticity and correspondence. Finally, based on this large-scale database, we conduct a benchmark experiment to evaluate the performance of several state-of-the-art IQA metrics on our constructed database.

함께 살펴볼 논문

공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.

arXiv:2506.23044공통 태그 Image Generation, Text-to-Image Generation, Text to Image Generation공식 코드 · Papers with Code

Ovis-U1 Technical Report

토론

이 논문과 연결된 포럼 글이 아직 없습니다.