AdaptFly: Prompt-Guided Adaptation of Foundation Models for Low-Altitude UAV Networks

• URL: http://arxiv.org/abs/2511.11720v1
• Date: Thu, 13 Nov 2025 00:20:37 GMT
• Title: AdaptFly: Prompt-Guided Adaptation of Foundation Models for Low-Altitude UAV Networks
• Authors: Jiao Chen, Haoyi Wang, Jianhua Tang, Junyi Wang,
• Abstract summary: Low-altitude Unmanned Aerial Vehicle (UAV) networks rely on robust semantic segmentation as a foundational enabler for distributed sensing-communication-control co-design.<n>We propose AdaptFly, a prompt-guided test-time adaptation framework that adjusts segmentation models without weight updates.<n>Experiments on UAVid and VDD benchmarks, along with real-world UAV deployments under diverse weather conditions, demonstrate that AdaptFly significantly improves segmentation accuracy and robustness.
• Score: 10.80018338292861
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Low-altitude Unmanned Aerial Vehicle (UAV) networks rely on robust semantic segmentation as a foundational enabler for distributed sensing-communication-control co-design across heterogeneous agents within the network. However, segmentation foundation models deteriorate quickly under weather, lighting, and viewpoint drift. Resource-limited UAVs cannot run gradient-based test-time adaptation, while resource-massive UAVs adapt independently, wasting shared experience. To address these challenges, we propose AdaptFly, a prompt-guided test-time adaptation framework that adjusts segmentation models without weight updates. AdaptFly features two complementary adaptation modes. For resource-limited UAVs, it employs lightweight token-prompt retrieval from a shared global memory. For resource-massive UAVs, it uses gradient-free sparse visual prompt optimization via Covariance Matrix Adaptation Evolution Strategy. An activation-statistic detector triggers adaptation, while cross-UAV knowledge pool consolidates prompt knowledge and enables fleet-wide collaboration with negligible bandwidth overhead. Extensive experiments on UAVid and VDD benchmarks, along with real-world UAV deployments under diverse weather conditions, demonstrate that AdaptFly significantly improves segmentation accuracy and robustness over static models and state-of-the-art TTA baselines. The results highlight a practical path to resilient, communication-efficient perception in the emerging low-altitude economy.

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