{
  "id": "fsa-cdm",
  "title": "Contrast-augmented Diffusion Model with Fine-grained Sequence Alignment for Markup-to-Image Generation",
  "authors": [
    {
      "given": "Guojin",
      "family": "Zhong"
    },
    {
      "given": "Jin",
      "family": "Yuan"
    },
    {
      "given": "Pan",
      "family": "Wang"
    },
    {
      "given": "Kailun",
      "family": "Yang"
    },
    {
      "given": "Weili",
      "family": "Guan"
    },
    {
      "given": "Zhiyong",
      "family": "Li"
    }
  ],
  "year": 2023,
  "venue": "ACM Multimedia 2023",
  "paper_url": "https://arxiv.org/abs/2308.01147",
  "pdf_url": "https://arxiv.org/pdf/2308.01147",
  "summary": "FSA-CDM combines fine-grained cross-modal sequence alignment, contextual modeling, and a contrast-augmented diffusion objective for markup-to-image generation.",
  "connection": "A subsequent markup-to-image method that builds on the task and four datasets introduced by Deng, Kojima, and Rush, addressing alignment and contrastive feedback.",
  "bibtex_type": "inproceedings",
  "citation_key": "zhong2023contrastaugmented",
  "source_urls": [
    "https://arxiv.org/html/2308.01147v1"
  ],
  "arxiv_id": "2308.01147",
  "arxiv_first_posted": "2023-08-02",
  "updated_at": "2026-11-10",
  "record_url": "https://yuntiandeng.com/research/papers/fsa-cdm/"
}