AAPMT: AGI Assessment Through Prompt and Metric Transformer
Benhao Huang
The emergence of text-to-image models marks a significant milestone in the
evolution of AI-generated images (AGIs), expanding their use in diverse domains
like design, entertainment, and more. Despite these breakthroughs, the quality
of AGIs often remains suboptimal, highlighting the need for effective
evaluation methods. These methods are crucial for assessing the quality of
images relative to their textual descriptions, and they must accurately mirror
human perception. Substantial progress has been achieved in this domain, with
innovative techniques such as BLIP and DBCNN contributing significantly.
However, recent studies, including AGIQA-3K, reveal a notable discrepancy
between current methods and state-of-the-art (SOTA) standards. This gap
emphasizes the necessity for a more sophisticated and precise evaluation
metric. In response, our objective is to develop a model that could give
ratings for metrics, which focuses on parameters like perceptual quality,
authenticity, and the correspondence between text and image, that more closely
aligns with human perception. In our paper, we introduce a range of effective
methods, including prompt designs and the Metric Transformer. The Metric
Transformer is a novel structure inspired by the complex interrelationships
among various AGI quality metrics. The code is available at
https://github.com/huskydoge/CS3324-Digital-Image-Processing/tree/main/Assignment1