Learning Aesthetic Preferences in Contextually Related Image Collections

The rapid advancement of smart devices and social media has significantly increased the user demand for aesthetic applications such as album management, photo sharing, and recommendation. In these real-world scenarios, users generally select preferred photographs from a collection of images depicting the same scene, event, or subject, making aesthetic preference inherently context-dependent. However, existing aesthetic evaluation methods typically focus on either context-independently or highly similar image series, limiting their ability to capture preferences within contextually related image collections. To address this gap, we introduce ContextAes-40K, a novel dataset comprising 39,033 images organized into 8,069 contextually related collections, with pairwise aesthetic preference annotations within each collection. To further evaluate performance in real-world user-curated collections, we collect ContextAes-Album, an independent real-user album dataset with user-provided aesthetic rankings. Building upon ContextAes-40K, we propose ContextAes, an MLLM-based aesthetic model trained through a three-stage training strategy. Extensive experimental results show that ContextAes achieves state-of-the-art performance on ContextAes-40K, exhibits strong generalization ability on other aesthetic benchmarks, and also demonstrates superior zero-shot performance on ContextAes-Album, highlighting its ability to identify aesthetic preferences within contextually related image collections.