DroneFINE is a domain-aware PEFT approach for VLM-based drone detectors using foreground-aware multi-path adaptation and text-conditioned background suppression, outperforming standard PEFT and matching full fine-tuning on VisDrone and UAVDT with fewer trainable parameters.
International Journal of Computer Vision130(9), 2337–2348 (2022)
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3representative citing papers
SAMPLe adds dual gradient constraints (ERM alignment plus full-batch orthogonality) to SAM-style prompt learning and raises harmonic-mean base-to-new accuracy across CoOp, CoCoOp, MaPLe, TCP and CoPrompt.
citing papers explorer
-
DroneFINE: Domain-Aware Parameter-Efficient Fine-Tuning of Vision-Language Detectors for Drone Images
DroneFINE is a domain-aware PEFT approach for VLM-based drone detectors using foreground-aware multi-path adaptation and text-conditioned background suppression, outperforming standard PEFT and matching full fine-tuning on VisDrone and UAVDT with fewer trainable parameters.
-
SAMPLe: SAM-based Optimizer for Prompt Learning in VLMs
SAMPLe adds dual gradient constraints (ERM alignment plus full-batch orthogonality) to SAM-style prompt learning and raises harmonic-mean base-to-new accuracy across CoOp, CoCoOp, MaPLe, TCP and CoPrompt.
- AdaBoosting Text Prompts for Vision-Language Models