Generative text-to-image models are advancing at an unprecedented pace, continuously shifting the perceptual quality ceiling and rendering previously collected labels unreliable for newer generations. To address this, we present ELIQ, a Label-free Framework for Quality Assessment of Evolving AI-generated Images. Specifically, ELIQ focuses on visual quality and prompt-image alignment, automatically constructs positive and aspect-specific negative pairs to cover both conventional distortions and AIGC-specific distortion modes, enabling transferable supervision without human annotations. Building on these pairs, ELIQ adapts a pre-trained multimodal model into a quality-aware critic via instruction tuning and predicts two-dimensional quality using lightweight gated fusion and a Quality Query Transformer. Experiments across multiple benchmarks demonstrate that ELIQ consistently outperforms existing label-free methods, generalizes from AI-generated content (AIGC) to user-generated content (UGC) scenarios without modification, and paves the way for scalable and label-free quality assessment under continuously evolving generative models. The code will be released upon publication.

翻译：生成式文本到图像模型以前所未有的速度发展，不断刷新感知质量上限，使先前收集的标签对新世代生成图像不再可靠。为此，我们提出ELIQ——面向演进式AI生成图像的无标签质量评估框架。具体而言，ELIQ聚焦视觉质量与提示-图像对齐度，通过自动化构建正样本对和特定方面负样本对，覆盖传统失真与AIGC特有失真模式，实现无需人工标注的可迁移监督。基于这些样本对，ELIQ采用指令微调将预训练多模态模型适配为质量感知判别器，并利用轻量级门控融合与质量查询Transformer预测二维质量。跨多个基准的实验表明：ELIQ始终优于现有无标签方法，能在无需修改的情况下从AIGC场景泛化至UGC场景，为持续演进生成模型下的可扩展无标签质量评估开辟新路径。代码将在论文发表后公开。