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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.
Achieving a 3.36-fold speedup in action generation, DriftingVLA eliminates the need for iterative refinement while maintaining high performance in robot control tasks.