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The Active Cross-View Object Geo-Localization (ActiveGeo), where an agent sequentially selects new viewpoints and determines when to stop, aiming to improve localization with minimal observations is introduced, and an ActiveGeo framework with three-stage training is proposed.
MVLGeo, an efficient framework designed to unify multiple viewpoints and reduce model redundancy, achieves state-of-the-art performance, demonstrating robustness to input degradation and generalization across viewpoints.
The introduction of ChainSpace, a chained-reasoning paradigm that structures spatial reasoning as a state-preserving multi-round process, is introduced as an effective paradigm for more faithful evaluation and more data-efficient learning of spatial intelligence.
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