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arXiv · 2602.03558

ELIQ: A Label-Free Framework for Quality Assessment of Evolving AI-Generated Images

Xinyue Li·Zhiming Xu·Min Tang·Zhaolin Cai·Sijing Wu·Xiongkuo Min·Yitong Chen·Guangtao Zhai
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Abstract

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.

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Xinyue Li, Zhiming Xu, Min Tang, Zhaolin Cai, Sijing Wu, Xiongkuo Min, Yitong Chen, Guangtao Zhai. 2026-02-03. ELIQ: A Label-Free Framework for Quality Assessment of Evolving AI-Generated Images. https://arxiv.org/abs/2602.03558

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