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CrossUAV is introduced, a UAV benchmark for joint object detection and instance segmentation that provides a unified evaluation platform for cross-granularity task collaboration and Cross-Granularity Socialized Collaboration is proposed, a progressive and adaptive framework that regulates when, where, and how tasks exchange information across network hierarchies.
Knowledge transfer among heterogeneous object detectors can yield up to a 2.6 AP improvement, revolutionizing how we approach multi-teacher distillation in AI.
RACO boosts UAV navigation success rates by over 9% by transforming coarse goals into dynamic hypotheses that adapt to real-time visual contexts.
