Papers › Semi-parametric Image Synthesis

Semi-parametric Image Synthesis

29 Apr 2018CVPR 2018 6arXiv:1804.10992archive 2025-07-28

Xiaojuan Qi, Qifeng Chen, Jiaya Jia, Vladlen Koltun

We present a semi-parametric approach to photographic image synthesis from semantic layouts. The approach combines the complementary strengths of parametric and nonparametric techniques. The nonparametric component is a memory bank of image segments constructed from a training set of images. Given a novel semantic layout at test time, the memory bank is used to retrieve photographic references that are provided as source material to a deep network. The synthesis is performed by a deep network that draws on the provided photographic material. Experiments on multiple semantic segmentation datasets show that the presented approach yields considerably more realistic images than recent purely parametric techniques. The results are shown in the supplementary video at https://youtu.be/U4Q98lenGLQ

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xjqicuhk/SIMS officialtf report

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Tasks

Image GenerationImage-to-Image TranslationSemantic Segmentation

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Image-to-Image Translation ADE20K-Outdoor Labels-to-Photos SIMS Accuracy 74.7% #6 of 7 Archive leaderboard report
Image-to-Image Translation ADE20K-Outdoor Labels-to-Photos SIMS FID 67.7 #6 of 7 Archive leaderboard report
Image-to-Image Translation ADE20K-Outdoor Labels-to-Photos SIMS mIoU 13.1 #6 of 7 Archive leaderboard report
Image-to-Image Translation Cityscapes Labels-to-Photo SIMS FID 49.7 #12 of 21 Archive leaderboard report
Image-to-Image Translation Cityscapes Labels-to-Photo SIMS Per-pixel Accuracy 75.5% #12 of 21 Archive leaderboard report
Image-to-Image Translation Cityscapes Labels-to-Photo SIMS mIoU 47.2 #12 of 21 Archive leaderboard report

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