← 研踪 PaperTrailCVF · CVPR 2026:cvf:2f14e5444d1dec0aab87db2f17035697 · 记录 #1
Learning Personalized Photographic Style from Pairwise User Preferences
来源日期（仅到年份） 2026
来源记录日期 2026
雷达观测 2026-11-05 02:11
摘要
Photographic style preferences are deeply personal, varying across individuals in color and tonal aesthetics. We introduce Personalized Photographic Style (PPS) learning, where the goal is to capture a user's implicit preferences from comparative judgments and apply them consistently across diverse images. To establish a foundation for this problem, we present three contributions. First, we introduce PPSD, a dataset containing pairwise preference judgments from 767 users, each providing an average of 70 comparisons. To capture diverse style signals, images are sourced from professional edits, device pipelines, and generative models. Second, we explore several baseline models demonstrating the feasibility of adapting style transfer and enhancement approaches for preference learning. Third, we develop a comparative evaluation framework suited to the implicit nature of personal preferences. We will make our dataset publicly available, and hope this work serves as a foundation for advancing research in personalized photographic style learning.
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记录 #1 · 2026 · 最新观测版本
Learning Personalized Photographic Style from Pairwise User Preferences
Photographic style preferences are deeply personal, varying across individuals in color and tonal aesthetics. We introduce Personalized Photographic Style (PPS) learning, where the goal is to capture a user's implicit preferences from comparative judgments and apply them consistently across diverse images. To establish a foundation for this problem, we present three contributions. First, we introduce PPSD, a dataset containing pairwise preference judgments from 767 users, each providing an average of 70 comparisons. To capture diverse style signals, images are sourced from professional edits, device pipelines, and generative models. Second, we explore several baseline models demonstrating the feasibility of adapting style transfer and enhancement approaches for preference learning. Third, we develop a comparative evaluation framework suited to the implicit nature of personal preferences. We will make our dataset publicly available, and hope this work serves as a foundation for advancing research in personalized photographic style learning.
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