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Quality Assessment for AI Generated Images with Instruction Tuning by Wang, Jiarui; Duan, Huiyu; Zhai, Guangtao; Min, Xiongkuo is a scholarly article available to read on EtoBox.

What is Quality Assessment for AI Generated Images with Instruction Tuning about?

Artificial Intelligence Generated Content (AIGC) has grown rapidly in recent years, among which AI-based image generation has gained widespread attention due to its efficient and imaginative image creation ability. However, AI-generated Images (AIGIs) may not satisfy human preferences due to their unique distortions, which highlights the necessity to understand and evaluate human preferences for AIGIs. To this end, in this paper, we first establish a novel Image Quality Assessment (IQA) database for AIGIs, termed AIGCIQA2023+, which provides human visual preference scores and detailed preference explanations from three perspectives including quality, authenticity, and correspondence. Then, based on the constructed AIGCIQA2023+ database, this paper presents a MINT-IQA model to evaluate and explain human preferences for AIGIs from Multi-perspectives with INstruction Tuning. Specifically, the MINT-IQA model first learn and evaluate human preferences for AI-generated Images from multi-perspectives, then via the vision-language instruction tuning strategy, MINT-IQA attains powerful understanding and explanation ability for human visual preference on AIGIs, which can be used for feedback

Author

Wang, Jiarui; Duan, Huiyu; Zhai, Guangtao; Min, Xiongkuo

Published

2024

Language

EN