Shun Zhu — accepted AI-conference papers

2026

Cross-Domain AI-Generated Image Quality Assessment via Content-Distortion Awareness

Shun Zhu, Xichen Yang, Dechun Zhao, Tianshu Wang, Yan Zhang, Tianyin Li +1

IJCAI 2026

With the expanding use of artificial intelligence generated images (AGIs) in scenarios such as gaming, art, and film production, evaluating their quality is essential to ensure their practical utility. To guarantee effective quality measurement, both content and distortion must be considered. Howeve

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2026

Graph-Driven Domain Co-Adaptation for Cross-Domain Image Quality Assessment

Shun Zhu, Xichen Yang, Yan Zhang, Tianshu Wang, Zhongyuan Mao, Tianyin Li +2

AAAI 2026technical

As a typical information medium, images are widely utilized across various scenarios. Measuring image quality accurately is meaningful for the subsequent usability of images. However, significant variations exist in image types and distortion types in different scenarios. And, acquiring labeled imag

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