Traditional Image Aesthetic Assessment (IAA) methods mainly rely on regressing absolute Mean Opinion Scores (MOS). However, such a paradigm overlooks the inherently dynamic nature of human aesthetic perception, which relies on subconscious comparison against implicit visual references.
Low-light image enhancement algorithms (LIEAs) aim to improve the visibility of images captured under poor illumination. However, the enhancement process often introduces artifacts such as noise amplification, color shift, structural damage, and over-exposure, which degrade the perceptual quality of the enhanced images.
arXiv:2606. 16082v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) have been increasingly adopted for Image Quality Assessment (IQA).
By Guanyi Qin, Junjie Zhang, Chunming He, Yibing Fu, Jie Liang, Tianhe Wu, Lei Zhang
arXiv:2606. 26947v1 Announce Type: cross Abstract: While personalized image generation has achieved remarkable progress, multi-reference image generation (MRIG) remains a challenging task.
By Wenwang Huang, Yusen Fu, Junjie Wang, Mengfei Huang, Yulin Li, Gan Liu, Jing Cai, Yancheng He, Zhuotao Tian
arXiv:2608. 07570v1 Announce Type: cross Abstract: Explainable aesthetic image cropping requires not only localizing a visually pleasing crop but also explaining why it is preferred.
By Rui Yang, Wei Zhou, Dingyong Gou, Xiaohui Cui, Cong Li, Yinyin Gong, Yipo Huang, Jiliang Zhao
This paper presents an overview of the inaugural PortraitCraft Challenge, held as one of the official competitions at CVPR 2026. The challenge focuses on portrait composition understanding and generation, aiming to advance AI research in portrait aesthetics analysis and controllable image synthesis.