Finding Highlight Images In Your Albums:From Benchmark To MLLM

Album highlight recommendation (AHR) focuses on identifying images from personal collections that are most suitable for personal enjoyment and social sharing. However, due to the inherent subjectivity and ambiguity involved in selecting highlights, AHR research has long been hindered by limited data availability, resulting in relatively slow progress in the field. To mitigate this issue, we first engage multiple annotators to re-annotate the public PEC and CUFED album datasets, producing high-quality labels for 910 albums comprising 87K images. These binary annotations are then aggregated into finegrained highlight scores to reduce individual annotation bias. To assess how well public consensus aligns with personal preferences, we further collect 10K images from 100 volunteers and invite the album owners themselves to provide personalized highlight annotations. This newly constructed dataset is named C-AHR-100. In addition to contributing these three benchmark datasets, we propose Highlight4U, an agentic multi-modal large language model (MLLM) framework specifically optimized for AHR. Highlight4U first identifies highlight-worthy reference images through localized scoring of individual images, and then refines its recommendations by incorporating contextual and semantic information across the reference set. Extensive experiments demonstrate that Highlight4U effectively captures public highlight perception and can further generalize to accommodate the personalized preferences of individual album owners. The source code and dataset are provided in the https://github.com/naninasm/Finding-Highlight-Images-InYour-Albums-From-Benchmark-To-MLLM.