Scene Graph Generation
130 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.
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Use these libraries to find Scene Graph Generation models and implementationsLatest papers with no code
Tri-modal Confluence with Temporal Dynamics for Scene Graph Generation in Operating Rooms
A comprehensive understanding of surgical scenes allows for monitoring of the surgical process, reducing the occurrence of accidents and enhancing efficiency for medical professionals.
AUG: A New Dataset and An Efficient Model for Aerial Image Urban Scene Graph Generation
To fill in the gap of the overhead view dataset, this paper constructs and releases an aerial image urban scene graph generation (AUG) dataset.
Weakly-Supervised 3D Scene Graph Generation via Visual-Linguistic Assisted Pseudo-labeling
However, previous 3D scene graph generation methods utilize a fully supervised learning manner and require a large amount of entity-level annotation data of objects and relations, which is extremely resource-consuming and tedious to obtain.
Improving Scene Graph Generation with Relation Words' Debiasing in Vision-Language Models
After that, we ensemble VLMs with SGG models to enhance representation.
Mapping High-level Semantic Regions in Indoor Environments without Object Recognition
Robots require a semantic understanding of their surroundings to operate in an efficient and explainable way in human environments.
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.
Contextual Associated Triplet Queries for Panoptic Scene Graph Generation
The Panoptic Scene Graph generation (PSG) task aims to extract the triplets composed of subject, object, and relation based on panoptic segmentation.
CLIP-Driven Open-Vocabulary 3D Scene Graph Generation via Cross-Modality Contrastive Learning
Then to align the image with 3DSG the camera view is treated as a positive sample and other views as negatives.