TIGeR: A Unified Framework for Time, Images and Geo-location Retrieval

• URL: http://arxiv.org/abs/2603.24749v2
• Date: Sat, 28 Mar 2026 20:41:13 GMT
• Title: TIGeR: A Unified Framework for Time, Images and Geo-location Retrieval
• Abstract summary: We propose TIGeR, a unified framework for Time, Images and Geo-location Retrieval.<n>By preserving the underlying location identity, TIGeR enables retrieval based on where and when a scene was captured.<n>We show that TIGeR consistently outperforms strong baselines and state-of-the-art methods by up to 16% on time-of-year, 8% time-of-day prediction, and 14% in geo-time aware retrieval recall.
• Score: 47.16110829725784
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Many real-world applications in digital forensics, urban monitoring, and environmental analysis require jointly reasoning about visual appearance, location, and time. Beyond standard geo-localization and time-of-capture prediction, these applications increasingly demand more complex capabilities, such as retrieving an image captured at the same location as a query image but at a specified target time. We formalize this problem as Geo-Time Aware Image Retrieval and propose TIGeR, a unified framework for Time, Images and Geo-location Retrieval. TIGeR supports flexible input configurations (single-modality and multi-modality queries) and uses the same representation to perform (i) geo-localization, (ii) time-of-capture prediction, and (iii) geo-time-aware retrieval. By preserving the underlying location identity despite large appearance changes, TIGeR enables retrieval based on where and when a scene was captured, rather than purely on visual similarity. To support this task, we design a multistage data curation pipeline and propose a new diverse dataset of 4.5M paired image-location-time triplets for training and 86k high-quality triplets for evaluation. Extensive experiments show that TIGeR consistently outperforms strong baselines and state-of-the-art methods by up to 16% on time-of-year, 8% time-of-day prediction, and 14% in geo-time aware retrieval recall, highlighting the benefits of unified geo-temporal modeling.

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