[2603.01493] PhotoBench: Beyond Visual Matching Towards Personalized Intent-Driven Photo Retrieval

Computer Science > Information Retrieval arXiv:2603.01493 (cs) [Submitted on 2 Mar 2026] Title:PhotoBench: Beyond Visual Matching Towards Personalized Intent-Driven Photo Retrieval Authors:Tianyi Xu, Rong Shan, Junjie Wu, Jiadeng Huang, Teng Wang, Jiachen Zhu, Wenteng Chen, Minxin Tu, Quantao Dou, Zhaoxiang Wang, Changwang Zhang, Weinan Zhang, Jun Wang, Jianghao Lin View a PDF of the paper titled PhotoBench: Beyond Visual Matching Towards Personalized Intent-Driven Photo Retrieval, by Tianyi Xu and 13 other authors View PDF HTML (experimental) Abstract:Personal photo albums are not merely collections of static images but living, ecological archives defined by temporal continuity, social entanglement, and rich metadata, which makes the personalized photo retrieval non-trivial. However, existing retrieval benchmarks rely heavily on context-isolated web snapshots, failing to capture the multi-source reasoning required to resolve authentic, intent-driven user queries. To bridge this gap, we introduce PhotoBench, the first benchmark constructed from authentic, personal albums. It is designed to shift the paradigm from visual matching to personalized multi-source intent-driven reasoning. Based on a rigorous multi-source profiling framework, which integrates visual semantics, spatial-temporal metadata, social identity, and temporal events for each image, we synthesize complex intent-driven queries rooted in users' life trajectories. Extensive evaluation on PhotoBench exposes two ...