Personalized image aesthetic assessment (PIAA) seeks to model, at the individual level, the subjective nature of aesthetic judgments toward artworks and photographs. Aesthetic preference is known to be both deeply personal and partially consistent across visual domains. Yet existing PIAA datasets and methods are largely confined to a single domain, or provide too few samples per annotator within each domain to enable personalization across domains. Consequently, the cross-domain generalization of personalized aesthetic preferences remains largely unexplored. To address this gap, we introduce XPASS-Vis, the first dataset explicitly designed for cross-domain PIAA. XPASS-Vis comprises 6,526 stimuli from three visual domains -- art, fashion, and landscape -- rated by 129 annotators, yielding 87,836 user-stimulus interactions, each annotated with an overall aesthetic score and nine aesthetic-emotion ratings. Notably, each annotator rated more than 200 stimuli per domain, providing sufficient per-domain coverage to support personalization both within and across domains. Moreover, we establish baseline models for cross-domain PIAA under unsupervised domain adaptation (UDA), where a model trained on a labeled source domain is transferred to an unlabeled target domain. A systematic evaluation of representative UDA approaches shows that the best-performing method recovers approximately 60\% (Spearman's $ρ$ = .28) of the supervised upper bound under a fully unsupervised setting. This provides encouraging evidence that personalized aesthetic preferences are, to a meaningful extent, transferable across visual domains. At the same time, a substantial gap remains, highlighting the need for PIAA-specific adaptation strategies. XPASS-Vis and the accompanying baselines provide a foundation for future research on cross-domain PIAA. All datasets and code will be made publicly available upon acceptance.

翻译：个性化图像美学评估（PIAA）旨在个体层面建模对艺术品与照片美学判断的主观特性。已知美学偏好既具有深度个体性，又在不同视觉领域间存在部分一致性。然而，现有PIAA数据集与方法大多局限于单一领域，或每个领域内每名标注者的样本量过少，无法实现跨域个性化。因此，个性化美学偏好的跨域泛化能力仍基本未被探索。为填补这一空白，我们提出XPASS-Vis——首个专为跨域PIAA设计的数据集。XPASS-Vis包含来自艺术、时尚与风景三个视觉领域的6526个刺激样本，由129名标注者评分，形成87836条用户-刺激交互记录，每条记录均包含整体美学评分与九项美学情绪评级。值得注意的是，每名标注者在每个领域内评分超过200个刺激样本，提供了充足的领域内覆盖以支持领域内与跨域个性化。此外，我们建立了基于无监督域适应（UDA）的跨域PIAA基线模型——该模型将基于标注源域训练的模型迁移至无标注目标域。对代表性UDA方法的系统评估表明，在完全无监督设置下，最优方法恢复的监督上限性能约为60%（斯皮尔曼相关系数ρ=0.28）。这为个性化美学偏好在视觉领域间具有显著迁移性提供了令人鼓舞的证据。然而，性能差距依然显著，凸显了对PIAA专用适配策略的需求。XPASS-Vis及其配套基线为跨域PIAA的未来研究奠定了基础。所有数据集与代码将在论文被接收后公开。