UAV Tracking with Multi-step Temporal Modeling Framework
MT-Track introduces a streamlined multi-step temporal modeling framework for enhanced UAV tracking, leveraging historical frames for precise target location.
SAM-DA: UAV Tracks Anything at Night with SAM-Powered Domain Adaptation
SAM-DA proposes a novel framework for real-time nighttime UAV tracking using SAM-powered domain adaptation, enhancing training sample quality and reducing the reliance on raw images.
SAM-DA: UAV Tracks Anything at Night with SAM-Powered Domain Adaptation
SAM-DA introduces a novel framework for real-time nighttime UAV tracking, leveraging the Segment Anything Model (SAM) to generate high-quality target domain training samples.
DCPT: Darkness Clue-Prompted Tracking in Nighttime UAVs
Efficiently learning to generate darkness clue prompts for robust UAV tracking at night.
Efficient Multi-step Temporal Modeling for UAV Tracking
The author introduces MT-Track, a multi-step temporal modeling framework for UAV tracking, leveraging temporal context to enhance tracking efficiency and precision.
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