Conceitos Básicos
DetDiffusion harmonizes generative and perceptive models to enhance data generation for perceptive tasks.
Resumo
Current perceptive models rely on resource-intensive datasets.
DetDiffusion combines generative and perceptive models for effective data generation.
Introduction discusses the importance of high-quality datasets in model effectiveness.
Method section details perception-aware loss (P.A. loss) and perception-aware attribute (P.A. Attr).
Experiments show DetDiffusion's superior performance in layout-guided generation.
Trainability results demonstrate significant improvements in detector training with synthetic data.
Qualitative results showcase fidelity, easy vs. hard attributes, and the impact on detection processes.
Ablation study confirms the importance of P.A. loss and P.A. Attr components in DetDiffusion's success.
Estatísticas
現在の知覚モデルはリソース集約型のデータセットに依存しています。
DetDiffusionは効果的なデータ生成のために生成モデルと知覚モデルを組み合わせます。
導入部では、モデルの効果における高品質なデータセットの重要性について説明しています。