Zewen Chen, Juan Wang, Bing Li, et al.

TL;DR: We introduce a prompt-driven no-reference IQA framework that adapts to diverse, unseen assessment requirements by encoding ARs from a few image-score exemplar prompts. To prevent shortcut learning, we train with hybrid AR augmentation (monotonic relabeling + Gaussian latent AR sampling), yielding AR-consistent predictions.