Coarse-to-Fine Hierarchical Alignment for UAV-based Human Detection using Diffusion Models
Researchers have developed a novel three-stage diffusion model framework called Coarse-to-Fine Hierarchical Alignment (CFHA) to improve human detection in drone imagery. This method addresses the challenge of domain gap between synthetic and real-world data by using diffusion models for style transfer and local refinement. CFHA aims to enhance the accuracy of object detectors trained on synthetic data, leading to significant improvements in detection performance on public benchmarks. AI
IMPACT Enhances drone-based human detection accuracy by bridging the synthetic-to-real data gap using diffusion models.