mlsanityData to 7 Oct 2026

SGDraw: Scene Graph Drawing Interface Using Object-Oriented Representation

arXiv 2211.16697 ↗First posted 30 Nov 2022cs.CV cs.HCcited by 0

Not on the map. Why?

Abstract (arXiv)

Scene understanding is an essential and challenging task in computer vision. To provide the visually fundamental graphical structure of an image, the scene graph has received increased attention due to its powerful semantic representation. However, it is difficult to draw a proper scene graph for image retrieval, image generation, and multi-modal applications. The conventional scene graph annotation interface is not easy to use in image annotations, and the automatic scene graph generation approaches using deep neural networks are prone to generate redundant content while disregarding details. In this work, we propose SGDraw, a scene graph drawing interface using object-oriented scene graph representation to help users draw and edit scene graphs interactively. For the proposed object-oriented representation, we consider the objects, attributes, and relationships of objects as a structural unit. SGDraw provides a web-based scene graph annotation and generation tool for scene understanding applications. To verify the effectiveness of the proposed interface, we conducted a comparison study with the conventional tool and the user experience study. The results show that SGDraw can help generate scene graphs with richer details and describe the images more accurately than traditional bounding box annotations. We believe the proposed SGDraw can be useful in various vision tasks, such as image retrieval and generation.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 26 of its 36 references that are in the map. First posted on arXiv 30 Nov 2022.

Builds on

26 of 36 references.

References · 26

  1. 2016Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotationscited by 2,646
  2. 2017Scene Graph Generation by Iterative Message Passingcited by 462
  3. 2017Neural Motifs: Scene Graph Parsing with Global Contextcited by 413
  4. 2020Unbiased Scene Graph Generation from Biased Trainingcited by 355
  5. 2016Visual Relationship Detection with Language Priorscited by 333
  6. 2018Image Generation from Scene Graphscited by 324
  7. 2018Graph R-CNN for Scene Graph Generationcited by 296
  8. 2017Scene Graph Generation from Objects, Phrases and Region Captionscited by 154
  9. 2021Energy-Based Learning for Scene Graph Generationcited by 104
  10. 2018Explainable and Explicit Visual Reasoning over Scene Graphscited by 103
  11. 2019Graphical Contrastive Losses for Scene Graph Parsingcited by 100
  12. 2017Detecting Visual Relationships with Deep Relational Networkscited by 97
14 more
  1. 2020Say As You Wish: Fine-grained Control of Image Caption Generation with Abstract Scene Graphscited by 87
  2. 2018Factorizable Net: An Efficient Subgraph-based Framework for Scene Graph Generationcited by 78
  3. 2022Scene Graph Generation: A Comprehensive Surveycited by 73
  4. 2018Attentive Relational Networks for Mapping Images to Scene Graphscited by 72
  5. 2020Scene Graph Reasoning for Visual Question Answeringcited by 49
  6. 2020Graph Density-Aware Losses for Novel Compositions in Scene Graph Generationcited by 31
  7. 2017Deep Variation-structured Reinforcement Learning for Visual Relationship and Attribute Detectioncited by 28
  8. 2022Reasoning with Scene Graphs for Robot Planning under Partial Observabilitycited by 25
  9. 2018Detecting Visual Relationships Using Box Attentioncited by 16
  10. 2020Generative Compositional Augmentations for Scene Graph Predictioncited by 14
  11. 2017ViP-CNN: Visual Phrase Guided Convolutional Neural Networkcited by 8
  12. 2021dualFace:Two-Stage Drawing Guidance for Freehand Portrait Sketchingcited by 8
  13. 2022DualSmoke: Sketch-Based Smoke Illustration Design with Two-Stage Generative Modelcited by 6
  14. 2021GeneAnnotator: A Semi-automatic Annotation Tool for Visual Scene Graphcited by 2

Built on by

Not cited yet.