DetDiffusion: Synergizing Generative and Perceptive Models for Enhanced
  Data Generation and Perception
Yibo Wang Ruiyuan Gao Kai Chen Kaiqiang Zhou Yingjie Cai Lanqing Hong Zhenguo Li Lihui Jiang Dit-Yan Yeung Qiang Xu Kai Zhang
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.