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We propose SegGen, a highly-effective training data generation method forimage segmentation, which pushes the performance limits of state-of-the-artsegmentation models to a significant extent. SegGen designs and integrates twodata generation strategies: MaskSyn and ImgSyn. (i) MaskSyn synthesizes newmask-image pairs via our proposed text-to-mask generation model andmask-to-image generation model, greatly improving the diversity in segmentationmasks for model supervision; (ii) ImgSyn synthesizes new images based onexisting masks using the mask-to-image generation model, strongly improvingimage diversity for model inputs. On the highly competitive ADE20K and COCObenchmarks, our data generation method markedly improves the performance ofstate-of-the-art segmentation models in semantic segmentation, panopticsegmentation, and instance segmentation. Notably, in terms of the ADE20K mIoU,Mask2Former R50 is largely boosted from 47.2 to 49.9 (+2.7); Mask2Former Swin-Lis also significantly increased from 56.1 to 57.4 (+1.3). These promisingresults strongly suggest the effectiveness of our SegGen even when abundanthuman-annotated training data is utilized. Moreover, training with oursynthetic data makes the segmentation models more robust towards unseendomains. Project website: https://seggenerator.github.io

SegGen: Supercharging Segmentation Models with Text2Mask and Mask2Img Synthesis