Image Synthesis with Graph Conditioning: CLIP-Guided Diffusion Models for Scene Graphs

• URL: http://arxiv.org/abs/2401.14111v3
• Date: Mon, 22 Jul 2024 07:38:19 GMT
• Title: Image Synthesis with Graph Conditioning: CLIP-Guided Diffusion Models for Scene Graphs
• Authors: Rameshwar Mishra, A V Subramanyam,
• Abstract summary: We introduce a novel approach to generate images from scene graphs. We leverage pre-trained text-to-image diffusion models and CLIP guidance to translate graph knowledge into images. Elaborate experiments reveal that our method outperforms existing methods on standard benchmarks.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Advancements in generative models have sparked significant interest in generating images while adhering to specific structural guidelines. Scene graph to image generation is one such task of generating images which are consistent with the given scene graph. However, the complexity of visual scenes poses a challenge in accurately aligning objects based on specified relations within the scene graph. Existing methods approach this task by first predicting a scene layout and generating images from these layouts using adversarial training. In this work, we introduce a novel approach to generate images from scene graphs which eliminates the need of predicting intermediate layouts. We leverage pre-trained text-to-image diffusion models and CLIP guidance to translate graph knowledge into images. Towards this, we first pre-train our graph encoder to align graph features with CLIP features of corresponding images using a GAN based training. Further, we fuse the graph features with CLIP embedding of object labels present in the given scene graph to create a graph consistent CLIP guided conditioning signal. In the conditioning input, object embeddings provide coarse structure of the image and graph features provide structural alignment based on relationships among objects. Finally, we fine tune a pre-trained diffusion model with the graph consistent conditioning signal with reconstruction and CLIP alignment loss. Elaborate experiments reveal that our method outperforms existing methods on standard benchmarks of COCO-stuff and Visual Genome dataset.

Related papers

• From Pixels to Graphs: Open-Vocabulary Scene Graph Generation with Vision-Language Models [81.92098140232638]
  Scene graph generation (SGG) aims to parse a visual scene into an intermediate graph representation for downstream reasoning tasks. Existing methods struggle to generate scene graphs with novel visual relation concepts. We introduce a new open-vocabulary SGG framework based on sequence generation.
  arXiv  Detail & Related papers  (2024-04-01T04:21:01Z)
• Joint Generative Modeling of Scene Graphs and Images via Diffusion Models [37.788957749123725]
  We present a novel generative task: joint scene graph - image generation. We introduce a novel diffusion model, DiffuseSG, that jointly models the adjacency matrix along with heterogeneous node and edge attributes. With a graph transformer being the denoiser, DiffuseSG successively denoises the scene graph representation in a continuous space and discretizes the final representation to generate the clean scene graph.
  arXiv  Detail & Related papers  (2024-01-02T10:10:29Z)
• SPAN: Learning Similarity between Scene Graphs and Images with Transformers [29.582313604112336]
  We propose a Scene graPh-imAge coNtrastive learning framework, SPAN, that can measure the similarity between scene graphs and images. We introduce a novel graph serialization technique that transforms a scene graph into a sequence with structural encodings.
  arXiv  Detail & Related papers  (2023-04-02T18:13:36Z)
• Transformer-based Image Generation from Scene Graphs [11.443097632746763]
  Graph-structured scene descriptions can be efficiently used in generative models to control the composition of the generated image. Previous approaches are based on the combination of graph convolutional networks and adversarial methods for layout prediction and image generation. We show how employing multi-head attention to encode the graph information can improve the quality of the sampled data.
  arXiv  Detail & Related papers  (2023-03-08T14:54:51Z)
• Diffusion-Based Scene Graph to Image Generation with Masked Contrastive Pre-Training [112.94542676251133]
  We propose to learn scene graph embeddings by directly optimizing their alignment with images. Specifically, we pre-train an encoder to extract both global and local information from scene graphs. The resulting method, called SGDiff, allows for the semantic manipulation of generated images by modifying scene graph nodes and connections.
  arXiv  Detail & Related papers  (2022-11-21T01:11:19Z)
• Scene Graph Modification as Incremental Structure Expanding [61.84291817776118]
  We focus on scene graph modification (SGM), where the system is required to learn how to update an existing scene graph based on a natural language query. We frame SGM as a graph expansion task by introducing the incremental structure expanding (ISE) We construct a challenging dataset that contains more complicated queries and larger scene graphs than existing datasets.
  arXiv  Detail & Related papers  (2022-09-15T16:26:14Z)
• Iterative Scene Graph Generation [55.893695946885174]
  Scene graph generation involves identifying object entities and their corresponding interaction predicates in a given image (or video) Existing approaches to scene graph generation assume certain factorization of the joint distribution to make the estimation iteration feasible. We propose a novel framework that addresses this limitation, as well as introduces dynamic conditioning on the image.
  arXiv  Detail & Related papers  (2022-07-27T10:37:29Z)
• Learning to Generate Scene Graph from Natural Language Supervision [52.18175340725455]
  We propose one of the first methods that learn from image-sentence pairs to extract a graphical representation of localized objects and their relationships within an image, known as scene graph. We leverage an off-the-shelf object detector to identify and localize object instances, match labels of detected regions to concepts parsed from captions, and thus create "pseudo" labels for learning scene graph.
  arXiv  Detail & Related papers  (2021-09-06T03:38:52Z)
• Unconditional Scene Graph Generation [72.53624470737712]
  We develop a deep auto-regressive model called SceneGraphGen which can learn the probability distribution over labelled and directed graphs. We show that the scene graphs generated by SceneGraphGen are diverse and follow the semantic patterns of real-world scenes.
  arXiv  Detail & Related papers  (2021-08-12T17:57:16Z)
• A Deep Local and Global Scene-Graph Matching for Image-Text Retrieval [4.159666152160874]
  Scene graph presentation is a suitable method for the image-text matching challenge. We introduce the Local and Global Scene Graph Matching (LGSGM) model that enhances the state-of-the-art method. Our enhancement with the combination of levels can improve the performance of the baseline method by increasing the recall by more than 10% on the Flickr30k dataset.
  arXiv  Detail & Related papers  (2021-06-04T10:33:14Z)

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.