SPAN: Learning Similarity between Scene Graphs and Images with Transformers

• URL: http://arxiv.org/abs/2304.00590v2
• Date: Mon, 20 May 2024 08:03:47 GMT
• Title: SPAN: Learning Similarity between Scene Graphs and Images with Transformers
• Authors: Yuren Cong, Wentong Liao, Bodo Rosenhahn, Michael Ying Yang,
• Abstract summary: 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.
• Score: 29.582313604112336
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Learning similarity between scene graphs and images aims to estimate a similarity score given a scene graph and an image. There is currently no research dedicated to this task, although it is critical for scene graph generation and downstream applications. Scene graph generation is conventionally evaluated by Recall$@K$ and mean Recall$@K$, which measure the ratio of predicted triplets that appear in the human-labeled triplet set. However, such triplet-oriented metrics fail to demonstrate the overall semantic difference between a scene graph and an image and are sensitive to annotation bias and noise. Using generated scene graphs in the downstream applications is therefore limited. To address this issue, for the first time, we propose a Scene graPh-imAge coNtrastive learning framework, SPAN, that can measure the similarity between scene graphs and images. Our novel framework consists of a graph Transformer and an image Transformer to align scene graphs and their corresponding images in the shared latent space. We introduce a novel graph serialization technique that transforms a scene graph into a sequence with structural encodings. Based on our framework, we propose R-Precision measuring image retrieval accuracy as a new evaluation metric for scene graph generation. We establish new benchmarks on the Visual Genome and Open Images datasets. Extensive experiments are conducted to verify the effectiveness of SPAN, which shows great potential as a scene graph encoder.

Related papers

• Image Synthesis with Graph Conditioning: CLIP-Guided Diffusion Models for Scene Graphs [0.0]
  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.
  arXiv  Detail & Related papers  (2024-01-25T11:46:31Z)
• 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)
• Iterative Scene Graph Generation with Generative Transformers [6.243995448840211]
  Scene graphs provide a rich, structured representation of a scene by encoding the entities (objects) and their spatial relationships in a graphical format. Current approaches take a generation-by-classification approach where the scene graph is generated through labeling of all possible edges between objects in a scene. This work introduces a generative transformer-based approach to generating scene graphs beyond link prediction.
  arXiv  Detail & Related papers  (2022-11-30T00:05:44Z)
• 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)
• 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)
• Image Scene Graph Generation (SGG) Benchmark [58.33119409657256]
  There is a surge of interest in image scene graph generation (object, and relationship detection) Due to the lack of a good benchmark, the reported results of different scene graph generation models are not directly comparable. We have developed a much-needed scene graph generation benchmark based on the maskrcnn-benchmark and several popular models.
  arXiv  Detail & Related papers  (2021-07-27T05:10:09Z)
• 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)
• Sketching Image Gist: Human-Mimetic Hierarchical Scene Graph Generation [98.34909905511061]
  We argue that a desirable scene graph should be hierarchically constructed, and introduce a new scheme for modeling scene graph. To generate a scene graph based on HET, we parse HET with a Hybrid Long Short-Term Memory (Hybrid-LSTM) which specifically encodes hierarchy and siblings context. To further prioritize key relations in the scene graph, we devise a Relation Ranking Module (RRM) to dynamically adjust their rankings.
  arXiv  Detail & Related papers  (2020-07-17T05:12:13Z)

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.