  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 to further
improve the assessment capabilities. Extensive experimental results demonstrate
that the proposed MINT-IQA model achieves state-of-the-art performance in
understanding and evaluating human visual preferences for AIGIs, and the
proposed model also achieves competing results on traditional IQA tasks
compared with state-of-the-art IQA models. The AIGCIQA2023+ database and
MINT-IQA model will be released to facilitate future research.
