Current perceptive models heavily depend on resource-intensive datasets,
prompting the need for innovative solutions. Leveraging recent advances in
diffusion models, synthetic data, by constructing image inputs from various
annotations, proves beneficial for downstream tasks. While prior methods have
separately addressed generative and perceptive models, DetDiffusion, for the
first time, harmonizes both, tackling the challenges in generating effective
data for perceptive models. To enhance image generation with perceptive models,
we introduce perception-aware loss (P.A. loss) through segmentation, improving
both quality and controllability. To boost the performance of specific
perceptive models, our method customizes data augmentation by extracting and
utilizing perception-aware attribute (P.A. Attr) during generation.
Experimental results from the object detection task highlight DetDiffusion's
superior performance, establishing a new state-of-the-art in layout-guided
generation. Furthermore, image syntheses from DetDiffusion can effectively
augment training data, significantly enhancing downstream detection
performance.

当前的感知模型依赖于资源密集型的数据集，因此需要创新性的解决方案。通过利用扩散模型和合成数据，通过从各种注释中构建图像输入，有助于下游任务。DetDiffusion 首次统一了生成模型和感知模型，解决了为感知模型生成有效数据的挑战。为了增强感知模型的图像生成能力，我们引入了感知感知损失（P.A. 损失），通过分割改善图像的质量和可控性。为了提高特定感知模型的性能，我们的方法在生成过程中提取和利用感知感知属性（P.A. 属性）进行自定义数据增强。目标检测任务的实验结果突出了 DetDiffusion 卓越的性能，在布局引导生成方面建立了新的最先进技术水平。此外，DetDiffusion 生成的图像合成能够有效增强训练数据，显著提高下游检测性能。