title: LAION-SG: An Enhanced Large-Scale Dataset for Training Complex
    Image-Text Models with Structural Annotations
summary: Recent advances in text-to-image (T2I) generation have shown remarkable
  success in producing high-quality images from text. However, existing T2I
  models show decayed performance in compositional image generation involving
  multiple objects and intricate relationships. We attribute this problem to
  limitations in existing datasets of image-text pairs, which lack precise
  inter-object relationship annotations with prompts only. To address this
  problem, we construct LAION-SG, a large-scale dataset with high-quality
  structural annotations of scene graphs (SG), which precisely describe
  attributes and relationships of multiple objects, effectively representing the
  semantic structure in complex scenes. Based on LAION-SG, we train a new
  foundation model SDXL-SG to incorporate structural annotation information into
  the generation process. Extensive experiments show advanced models trained on
  our LAION-SG boast significant performance improvements in complex scene
  generation over models on existing datasets. We also introduce CompSG-Bench, a
  benchmark that evaluates models on compositional image generation, establishing
  a new standard for this domain.
paper_authors._id: 675a6094ca1f2b4cd6b2bfac
paper_authors.name: Zejian Li
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paper_authors.name: Chenye Meng
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paper_authors.name: Yize Li
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paper_authors.fullname: Yize Li
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paper_authors.name: Ling Yang
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paper_authors.fullname: Ling Yang
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paper_authors.name: Shengyuan Zhang
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paper_authors.fullname: Shengyuan Zhang
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paper_authors.user: SYZhang0805
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paper_authors.name: Jiarui Ma
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paper_authors.fullname: JIARUI MA
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paper_authors.user: MAJIARUI
paper_authors._id: 675a6094ca1f2b4cd6b2bfb2
paper_authors.name: Jiayi Li
paper_authors._id: 675a6094ca1f2b4cd6b2bfb3
paper_authors.name: Guang Yang
paper_authors._id: 675a6094ca1f2b4cd6b2bfb4
paper_authors.name: Changyuan Yang
paper_authors._id: 675a6094ca1f2b4cd6b2bfb5
paper_authors.name: Zhiyuan Yang
paper_authors._id: 675a6094ca1f2b4cd6b2bfb6
paper_authors.name: Jinxiong Chang
paper_authors._id: 675a6094ca1f2b4cd6b2bfb7
paper_authors.name: Lingyun Sun
paper_authors.status: admin_assigned
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paper_authors.fullname: Lingyun Sun
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paper_authors.user: slysun
paper_title: LAION-SG: An Enhanced Large-Scale Dataset for Training Complex
    Image-Text Models with Structural Annotations
paper_summary: Recent advances in text-to-image (T2I) generation have shown remarkable
  success in producing high-quality images from text. However, existing T2I
  models show decayed performance in compositional image generation involving
  multiple objects and intricate relationships. We attribute this problem to
  limitations in existing datasets of image-text pairs, which lack precise
  inter-object relationship annotations with prompts only. To address this
  problem, we construct LAION-SG, a large-scale dataset with high-quality
  structural annotations of scene graphs (SG), which precisely describe
  attributes and relationships of multiple objects, effectively representing the
  semantic structure in complex scenes. Based on LAION-SG, we train a new
  foundation model SDXL-SG to incorporate structural annotation information into
  the generation process. Extensive experiments show advanced models trained on
  our LAION-SG boast significant performance improvements in complex scene
  generation over models on existing datasets. We also introduce CompSG-Bench, a
  benchmark that evaluates models on compositional image generation, establishing
  a new standard for this domain.
paper_ai_keywords: text-to-image (T2I) generation compositional image generation image-text pairs inter-object relationship annotations scene graphs (SG) attributes and relationships semantic structure complex scenes foundation model LAION-SG SDXL-SG structural annotation information generation process CompSG-Bench benchmark compositional image generation