Image Synthesis via Semantic Composition
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Abstract (arXiv)
In this paper, we present a novel approach to synthesize realistic images based on their semantic layouts. It hypothesizes that for objects with similar appearance, they share similar representation. Our method establishes dependencies between regions according to their appearance correlation, yielding both spatially variant and associated representations. Conditioning on these features, we propose a dynamic weighted network constructed by spatially conditional computation (with both convolution and normalization). More than preserving semantic distinctions, the given dynamic network strengthens semantic relevance, benefiting global structure and detail synthesis. We demonstrate that our method gives the compelling generation performance qualitatively and quantitatively with extensive experiments on benchmarks.
Key facts. Cited by 45 papers in the map (data through 7 Oct 2026). Builds on 41 of its 52 references that are in the map. First posted on arXiv 15 Sep 2021.
Builds on
41 of 52 references.
References · 41
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- 2014Adam: A Method for Stochastic Optimizationcited by 36,960
- 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
- 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,029
- 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,510
- 2016The Cityscapes Dataset for Semantic Urban Scene Understandingcited by 4,427
- 2016DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFscited by 3,239
- 2017Non-local Neural Networkscited by 2,290
- 2014Conditional Generative Adversarial Netscited by 2,152
- 2018Spectral Normalization for Generative Adversarial Networkscited by 1,618
- 2017Deformable Convolutional Networkscited by 1,481
- 2019Semantic Image Synthesis with Spatially-Adaptive Normalizationcited by 1,295
29 more
- 2018Unified Perceptual Parsing for Scene Understandingcited by 1,265
- 2017High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANscited by 1,176
- 2016HyperNetworkscited by 1,003
- 2016COCO-Stuff: Thing and Stuff Classes in Contextcited by 858
- 2018Self-Attention Generative Adversarial Networkscited by 809
- 2018Multimodal Unsupervised Image-to-Image Translationcited by 795
- 2017Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networkscited by 707
- 2019MaskGAN: Towards Diverse and Interactive Facial Image Manipulationcited by 624
- 2018Generative Image Inpainting with Contextual Attentioncited by 578
- 2016Dynamic Filter Networkscited by 318
- 2017Dilated Residual Networkscited by 293
- 2019CondConv: Conditionally Parameterized Convolutions for Efficient Inferencecited by 273
- 2019SEAN: Image Synthesis with Semantic Region-Adaptive Normalizationcited by 253
- 2017Photographic Image Synthesis with Cascaded Refinement Networkscited by 223
- 2019Meta-SR: A Magnification-Arbitrary Network for Super-Resolutioncited by 180
- 2020You Only Need Adversarial Supervision for Semantic Image Synthesiscited by 129
- 2019Learning to Predict Layout-to-image Conditional Convolutions for Semantic Image Synthesiscited by 108
- 2019Multi-Channel Attention Selection GAN with Cascaded Semantic Guidance for Cross-View Image Translationcited by 97
- 2018Image Inpainting via Generative Multi-column Convolutional Neural Networkscited by 81
- 2019Local Class-Specific and Global Image-Level Generative Adversarial Networks for Semantic-Guided Scene Generationcited by 75
- 2020TSIT: A Simple and Versatile Framework for Image-to-Image Translationcited by 64
- 2018Diverse Image Synthesis from Semantic Layouts via Conditional IMLEcited by 60
- 2018Semi-parametric Image Synthesiscited by 50
- 2020Dynamic Region-Aware Convolutioncited by 49
- 2019Multi-mapping Image-to-Image Translation via Learning Disentanglementcited by 35
- 2020VCNet: A Robust Approach to Blind Image Inpaintingcited by 25
- 2018MetaAnchor: Learning to Detect Objects with Customized Anchorscited by 21
- 2020PointINS: Point-based Instance Segmentationcited by 17
- 2020Attentive Normalization for Conditional Image Generationcited by 10
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Citing papers per year: 2021: 2; 2022: 10; 2023: 15; 2024: 11; 2025: 6; 2026: 1.
45 papers.
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