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Curating datasets for object segmentation is a difficult task. With theadvent of large-scale pre-trained generative models, conditional imagegeneration has been given a significant boost in result quality and ease ofuse. In this paper, we present a novel method that enables the generation ofgeneral foreground-background segmentation models from simple textualdescriptions, without requiring segmentation labels. We leverage and explorepre-trained latent diffusion models, to automatically generate weaksegmentation masks for concepts and objects. The masks are then used tofine-tune the diffusion model on an inpainting task, which enables fine-grainedremoval of the object, while at the same time providing a synthetic foregroundand background dataset. We demonstrate that using this method beats previousmethods in both discriminative and generative performance and closes the gapwith fully supervised training while requiring no pixel-wise object labels. Weshow results on the task of segmenting four different objects (humans, dogs,cars, birds) and a use case scenario in medical image analysis. The code isavailable at https://github.com/MischaD/fobadiffusion.

Foreground-Background Separation through Concept Distillation from Generative Image Foundation Models