Scene Graph Generation
110 papers with code • 5 benchmarks • 7 datasets
A scene graph is a structured representation of an image, where nodes in a scene graph correspond to object bounding boxes with their object categories, and edges correspond to their pairwise relationships between objects. The task of Scene Graph Generation is to generate a visually-grounded scene graph that most accurately correlates with an image.
Benchmarks
These leaderboards are used to track progress in Scene Graph Generation
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Libraries
Use these libraries to find Scene Graph Generation models and implementations
MS COCO
Visual Genome
VRD
3DSSG
3RScan
PSG Dataset
4D-OR
Latest papers with no code
Towards Scene Graph Anticipation
In SceneSayer, we leverage object-centric representations of relationships to reason about the observed video frames and model the evolution of relationships between objects.
S^2Former-OR: Single-Stage Bimodal Transformer for Scene Graph Generation in OR
In this study, we introduce a novel single-stage bimodal transformer framework for SGG in the OR, termed S^2Former-OR, aimed to complementally leverage multi-view 2D scenes and 3D point clouds for SGG in an end-to-end manner.
Towards Lifelong Scene Graph Generation with Knowledge-ware In-context Prompt Learning
Besides, extensive experiments on the two mainstream benchmark datasets, VG and Open-Image(v6), show the superiority of our proposed model to a number of competitive SGG models in terms of continuous learning and conventional settings.
TD^2-Net: Toward Denoising and Debiasing for Dynamic Scene Graph Generation
To address the above problems, in this paper, we introduce a network named TD$^2$-Net that aims at denoising and debiasing for dynamic SGG.
Joint Generative Modeling of Scene Graphs and Images via Diffusion Models
While previous works have explored image generation conditioned on scene graphs or layouts, our task is distinctive and important as it involves generating scene graphs themselves unconditionally from noise, enabling efficient and interpretable control for image generation.
Context-based Transfer and Efficient Iterative Learning for Unbiased Scene Graph Generation
Thus, we introduce a plug-and-play method named CITrans, which iteratively trains SGG models with progressively enhanced data.
Indoor and Outdoor 3D Scene Graph Generation via Language-Enabled Spatial Ontologies
This paper proposes an approach to build 3D scene graphs in arbitrary (indoor and outdoor) environments.
GPT4SGG: Synthesizing Scene Graphs from Holistic and Region-specific Narratives
Learning scene graphs from natural language descriptions has proven to be a cheap and promising scheme for Scene Graph Generation (SGG).
HIG: Hierarchical Interlacement Graph Approach to Scene Graph Generation in Video Understanding
In this paper, we delve into interactivities understanding within visual content by deriving scene graph representations from dense interactivities among humans and objects.
HAtt-Flow: Hierarchical Attention-Flow Mechanism for Group Activity Scene Graph Generation in Videos
Flow-Attention incorporates flow conservation principles, fostering competition for sources and allocation for sinks, effectively preventing the generation of trivial attention.
