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Frontiers of Information Technology & Electronic Engineering 

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Image generation evaluation: a comprehensive survey of human and automatic evaluations


Author(s):  Qi LIU1, Shuanglin YANG2, Zejian LI1, Lefan HOU3, Chenye MENG3, Ying ZHANG1, Lingyun SUN3

Affiliation(s):  1School of Software Technology, Zhejiang University, Ningbo 315100, China; more

Corresponding email(s):  zejianlee@zju.edu.cn

Key Words:  Image generation evaluation; Human evaluation; Automatic evaluation; Evaluation protocols; Evaluation aspects


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Qi LIU1, Shuanglin YANG2, Zejian LI1, Lefan HOU3, Chenye MENG3,Ying ZHANG1, Lingyun SUN3. Image generation evaluation: a comprehensive survey of human and automatic evaluations[J]. Frontiers of Information Technology & Electronic Engineering,in press.https://doi.org/10.1631/FITEE.2400904

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author="Qi LIU1, Shuanglin YANG2, Zejian LI1, Lefan HOU3, Chenye MENG3,Ying ZHANG1, Lingyun SUN3",
journal="Frontiers of Information Technology & Electronic Engineering",
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publisher="Zhejiang University Press & Springer",
doi="https://doi.org/10.1631/FITEE.2400904"
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Abstract: 
Image generation models have made remarkable progress, and image evaluation is crucial for explaining and driving the development of these models. Previous studies have extensively explored human and automatic evaluations of image generation. Herein, these studies are comprehensively surveyed, specifically for two main parts: evaluation protocols and evaluation methods. First, 10 image generation tasks are summarized by focusing on their differences in evaluation aspects. Based on this, a novel protocol is proposed to cover human and automatic evaluation aspects required for various image generation tasks. Second, the review of automatic evaluation methods in the past five years is highlighted. To our knowledge, this paper presents the first comprehensive summary of human evaluation, encompassing evaluation methods, tools, details, and data analysis methods. Finally, this the challenges and potential directions for image generation evaluation are discussed. We hope that this survey will help researchers to develop a systematic understanding of image generation evaluation, stay updated with the latest advancements in the field, and encourage further research.

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