SG-Adapter: Enhancing Text-to-Image Generation with Scene Graph Guidance

• URL: http://arxiv.org/abs/2405.15321v1
• Date: Fri, 24 May 2024 08:00:46 GMT
• Title: SG-Adapter: Enhancing Text-to-Image Generation with Scene Graph Guidance
• Authors: Guibao Shen, Luozhou Wang, Jiantao Lin, Wenhang Ge, Chaozhe Zhang, Xin Tao, Yuan Zhang, Pengfei Wan, Zhongyuan Wang, Guangyong Chen, Yijun Li, Ying-Cong Chen,
• Abstract summary: We introduce the Scene Graph Adapter(SG-Adapter), leveraging the structured representation of scene graphs to rectify inaccuracies in the original text embeddings. The SG-Adapter's explicit and non-fully connected graph representation greatly improves the fully connected, transformer-based text representations.
• Score: 46.77060502803466
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advancements in text-to-image generation have been propelled by the development of diffusion models and multi-modality learning. However, since text is typically represented sequentially in these models, it often falls short in providing accurate contextualization and structural control. So the generated images do not consistently align with human expectations, especially in complex scenarios involving multiple objects and relationships. In this paper, we introduce the Scene Graph Adapter(SG-Adapter), leveraging the structured representation of scene graphs to rectify inaccuracies in the original text embeddings. The SG-Adapter's explicit and non-fully connected graph representation greatly improves the fully connected, transformer-based text representations. This enhancement is particularly notable in maintaining precise correspondence in scenarios involving multiple relationships. To address the challenges posed by low-quality annotated datasets like Visual Genome, we have manually curated a highly clean, multi-relational scene graph-image paired dataset MultiRels. Furthermore, we design three metrics derived from GPT-4V to effectively and thoroughly measure the correspondence between images and scene graphs. Both qualitative and quantitative results validate the efficacy of our approach in controlling the correspondence in multiple relationships.

Related papers

• HeGraphAdapter: Tuning Multi-Modal Vision-Language Models with Heterogeneous Graph Adapter [19.557300178619382]
  We propose a novel Heterogeneous Graph Adapter to achieve tuning VLMs for the downstream tasks. We employ a specific Heterogeneous Graph Neural Network to excavate multi-modality structure knowledge for the downstream tasks. Experimental results on 11 benchmark datasets demonstrate the effectiveness and benefits of the proposed HeGraphAdapter.
  arXiv  Detail & Related papers  (2024-10-10T12:20:58Z)
• Scene Graph Disentanglement and Composition for Generalizable Complex Image Generation [44.457347230146404]
  We leverage the scene graph, a powerful structured representation, for complex image generation. We employ the generative capabilities of variational autoencoders and diffusion models in a generalizable manner. Our method outperforms recent competitors based on text, layout, or scene graph.
  arXiv  Detail & Related papers  (2024-10-01T07:02:46Z)
• Relation Rectification in Diffusion Model [64.84686527988809]
  We introduce a novel task termed Relation Rectification, aiming to refine the model to accurately represent a given relationship it initially fails to generate. We propose an innovative solution utilizing a Heterogeneous Graph Convolutional Network (HGCN) The lightweight HGCN adjusts the text embeddings generated by the text encoder, ensuring the accurate reflection of the textual relation in the embedding space.
  arXiv  Detail & Related papers  (2024-03-29T15:54:36Z)
• DSGG: Dense Relation Transformer for an End-to-end Scene Graph Generation [13.058196732927135]
  Scene graph generation aims to capture detailed spatial and semantic relationships between objects in an image. Existing Transformer-based methods either employ distinct queries for objects and predicates or utilize holistic queries for relation triplets. We present a new Transformer-based method, called DSGG, that views scene graph detection as a direct graph prediction problem.
  arXiv  Detail & Related papers  (2024-03-21T23:43:30Z)
• GPT4SGG: Synthesizing Scene Graphs from Holistic and Region-specific Narratives [69.36723767339001]
  We propose a novel framework named textitGPT4SGG to obtain more accurate and comprehensive scene graph signals. We show textitGPT4SGG significantly improves the performance of SGG models trained on image-caption data.
  arXiv  Detail & Related papers  (2023-12-07T14:11:00Z)
• FACTUAL: A Benchmark for Faithful and Consistent Textual Scene Graph Parsing [66.70054075041487]
  Existing scene graphs that convert image captions into scene graphs often suffer from two types of errors. First, the generated scene graphs fail to capture the true semantics of the captions or the corresponding images, resulting in a lack of faithfulness. Second, the generated scene graphs have high inconsistency, with the same semantics represented by different annotations.
  arXiv  Detail & Related papers  (2023-05-27T15:38:31Z)
• Coarse-to-Fine Contrastive Learning in Image-Text-Graph Space for Improved Vision-Language Compositionality [50.48859793121308]
  Contrastively trained vision-language models have achieved remarkable progress in vision and language representation learning. Recent research has highlighted severe limitations in their ability to perform compositional reasoning over objects, attributes, and relations.
  arXiv  Detail & Related papers  (2023-05-23T08:28:38Z)
• Text Reading Order in Uncontrolled Conditions by Sparse Graph Segmentation [71.40119152422295]
  We propose a lightweight, scalable and generalizable approach to identify text reading order. The model is language-agnostic and runs effectively across multi-language datasets. It is small enough to be deployed on virtually any platform including mobile devices.
  arXiv  Detail & Related papers  (2023-05-04T06:21:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.