mlsanityData to 14 Oct 2026

Unsupervised Domain Adaption with Pixel-level Discriminator for Image-aware Layout Generation

arXiv 2303.14377 ↗First posted 25 Mar 2023cs.CVcited by 13

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Abstract (arXiv)

Layout is essential for graphic design and poster generation. Recently, applying deep learning models to generate layouts has attracted increasing attention. This paper focuses on using the GAN-based model conditioned on image contents to generate advertising poster graphic layouts, which requires an advertising poster layout dataset with paired product images and graphic layouts. However, the paired images and layouts in the existing dataset are collected by inpainting and annotating posters, respectively. There exists a domain gap between inpainted posters (source domain data) and clean product images (target domain data). Therefore, this paper combines unsupervised domain adaption techniques to design a GAN with a novel pixel-level discriminator (PD), called PDA-GAN, to generate graphic layouts according to image contents. The PD is connected to the shallow level feature map and computes the GAN loss for each input-image pixel. Both quantitative and qualitative evaluations demonstrate that PDA-GAN can achieve state-of-the-art performances and generate high-quality image-aware graphic layouts for advertising posters.

Key facts. Cited by 13 papers in the map (data through 14 Oct 2026). Builds on 25 of its 35 references that are in the map. First posted on arXiv 25 Mar 2023.

Builds on

25 of 35 references.

References · 25

  1. 2017Attention Is All You Needcited by 60,361
  2. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  3. 2020End-to-End Object Detection with Transformerscited by 7,414
  4. 2015Rethinking the Inception Architecture for Computer Visioncited by 5,475
  5. 2016Feature Pyramid Networks for Object Detectioncited by 5,036
  6. 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,527
  7. 2015Domain-Adversarial Training of Neural Networkscited by 3,044
  8. 2021Resolution-robust Large Mask Inpainting with Fourier Convolutionscited by 738
  9. 2021Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformerscited by 320
  10. 2016Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networkscited by 308
  11. 2020A Brief Review of Domain Adaptationcited by 288
  12. 2016NIPS 2016 Tutorial: Generative Adversarial Networkscited by 285
13 more
  1. 2018Multi-Adversarial Domain Adaptationcited by 196
  2. 2019LayoutGAN: Generating Graphic Layouts with Wireframe Discriminatorscited by 131
  3. 2020LayoutTransformer: Layout Generation and Completion with Self-attentioncited by 123
  4. 2019LayoutVAE: Stochastic Scene Layout Generation From a Label Setcited by 119
  5. 2021Variational Transformer Networks for Layout Generationcited by 88
  6. 2019Neural Design Network: Graphic Layout Generation with Constraintscited by 83
  7. 2021Constrained Graphic Layout Generation via Latent Optimizationcited by 83
  8. 2020Attribute-conditioned Layout GAN for Automatic Graphic Designcited by 68
  9. 2022Composition-aware Graphic Layout GAN for Visual-textual Presentation Designscited by 59
  10. 2021Spectral Unsupervised Domain Adaptation for Visual Recognitioncited by 40
  11. 2022Geometry Aligned Variational Transformer for Image-conditioned Layout Generationcited by 33
  12. 2022Multi-Source Unsupervised Domain Adaptation via Pseudo Target Domaincited by 28
  13. 2022Domain Adaptation for Object Detection using SE Adaptors and Center Losscited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 2; 2024: 5; 2025: 3; 2026: 3.

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  1. 2024PosterLLaVa: Constructing a Unified Multi-modal Layout Generator with LLMcited by 41
  2. 2023Retrieval-Augmented Layout Transformer for Content-Aware Layout Generationcited by 35
  3. 2024LayoutFlow: Flow Matching for Layout Generationcited by 21
  4. 2023Planning and Rendering: Towards Product Poster Generation with Diffusion Modelscited by 16
  5. 2025PosterO: Structuring Layout Trees to Enable Language Models in Generalized Content-Aware Layout Generationcited by 16
  6. 2024Kinetic Typography Diffusion Modelcited by 9
  7. 2024InstructLayout: Instruction-Driven 2D and 3D Layout Synthesis with Semantic Graph Priorcited by 6
  8. 2025Scan-and-Print: Patch-level Data Summarization and Augmentation for Content-aware Layout Generation in Poster Designcited by 2
  9. 2026PSDesigner: Automated Graphic Design with a Human-Like Creative Workflowcited by 2
  10. 2024LayoutDiT: Exploring Content-Graphic Balance in Layout Generation with Diffusion Transformercited by 1
  11. 2026Nip Rumors in the Bud: Retrieval-Guided Topic-Level Adaptation for Test-Time Fake News Video Detectioncited by 1
  12. 2025UniLayDiff: A Unified Diffusion Transformer for Content-Aware Layout Generationcited by 0
1 more
  1. 2026GAN-based Domain Adaptation for Image-aware Layout Generation in Advertising Poster Designcited by 0