HiSymGeo: Hierarchical Context Symbiosis for Cross-View Object-Level Image Geo-Localization

Cross-view object-level image geo-localization (CVOIGL) aims to locate ground/drone-view query objects in satellite imagery. This task confronts two obstacles, namely view differences from imaging platform viewpoint changes and detection ambiguities from similar objects in large-scale satellite views. Existing methods typically employ uniform feature processing across objects while overlooking query-reference cross-view differences, leading to compromised localization precision when handling structurally analogous objects with scale variations. In this paper, we propose HiSymGeo, a Hierarchical Context Symbiosis framework with dual cooperative learning, achieving cross-view representation alignment and structural ambiguity resolution. Specifically, to mitigate cross-view differences, the Diversified View Enhancer (DiVE) first incorporates context-aware query enhancement for ground/drone-view representation while constructing scale-agnostic reference enhancement in satellite views to handle scale variations. These view-specific features then undergo contrastive learning via semantic-aware matching to align query and reference representations. Furthermore, the Query-Gated Multi-Expert View Fusion (QG-MEVF) introduces dynamic expert routing via multi-scale pyramidal representations, in which a Mixture-of-Experts (MoE) inspired architecture employs query-driven gating to adaptively select scale-specific fusion expert. This differentiable routing mechanism boosts structural discrimination against analogous objects, enabling precise object localization. Extensive ablation experiments demonstrate HiSymGeo's superiority, achieving state-of-the-art effectiveness while ensuring high cross-dataset generalization. We have released our code at https://github.com/chenqi142/HiSymGeo.