Dog-IQA: Standard-guided Zero-shot MLLM for Mix-grained Image Quality Assessment
<|reference_start|>Dog-IQA: Standard-guided Zero-shot MLLM for Mix-grained Image Quality Assessment: Image quality assessment (IQA) serves as the golden standard for all models' performance in nearly all computer vision fields. However, it still suffers from poor out-of-distribution generalization ability and expensive training costs. To address these problems, we propose Dog-IQA, a standard-guided zero-shot mix-grained IQA method, which is training-free and utilizes the exceptional prior knowledge of multimodal large language models (MLLMs). To obtain accurate IQA scores, namely scores consistent with humans, we design an MLLM-based inference pipeline that imitates human experts. In detail, Dog-IQA applies two techniques. First, Dog-IQA objectively scores with specific standards that utilize MLLM's behavior pattern and minimize the influence of subjective factors. Second, Dog-IQA comprehensively takes local semantic objects and the whole image as input and aggregates their scores, leveraging local and global information. Our proposed Dog-IQA achieves state-of-the-art (SOTA) performance compared with training-free methods, and competitive performance compared with training-based methods in cross-dataset scenarios. Our code will be available at https://github.com/Kai-Liu001/Dog-IQA.<|reference_end|>
@article{liu2024dog-iqa:,
    title={Dog-IQA: Standard-guided Zero-shot MLLM for Mix-grained Image Quality
  Assessment},
    author={Kai Liu, Ziqing Zhang, Wenbo Li, Renjing Pei, Fenglong Song, Xiaohong
  Liu, Linghe Kong, and Yulun Zhang},
    journal={arXiv preprint arXiv:2410.02505},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.02505},
    primaryClass={cs.CV cs.AI}
}