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The Unbearable Weight: Scaling Models and Methods for UAV Audio Classification
As unmanned aerial vehicles (UAVs) become increasingly prevalent in consumer and defense settings, classifying them reliably from limited, modality-specific data is an urgent challenge. The dominant approach, large pretrained networks fully fine-tuned on task data, carries a substantial computational and memory weight that is hard to bear in resource-constrained UAV deployments, where edge inference and rapid retraining for emerging platforms are both required. This paper systematically scales across both model architectures and fine-tuning methods for UAV audio classification, asking when tha
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-15T22:22:21.000Z
First collected: 2026-09-20T08:20:57.646Z. This is not the publication date.