Understanding and Evaluating Human Preferences for AI Generated Images with Instruction Tuning
<|reference_start|>Understanding and Evaluating Human Preferences for AI Generated Images with Instruction Tuning: 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.<|reference_end|>
@article{wang2024understanding,
    title={Understanding and Evaluating Human Preferences for AI Generated Images
  with Instruction Tuning},
    author={Jiarui Wang, Huiyu Duan, Guangtao Zhai, Xiongkuo Min},
    journal={arXiv preprint arXiv:2405.07346},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.07346},
    primaryClass={cs.CV}
}