Scene Graph Generation

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.

Source: Scene Graph Generation by Iterative Message Passing

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 61–70 of 318 papers

Title	Date	Tasks	Status	Hype
Panoptic Scene Graph Generation with Semantics-Prototype Learning	Jul 28, 2023	Graph GenerationPanoptic Scene Graph Generation	CodeCode Available	1
Panoptic Video Scene Graph Generation	Nov 28, 2023	Graph GenerationPanoptic Scene Graph Generation	CodeCode Available	1
Compositional Feature Augmentation for Unbiased Scene Graph Generation	Aug 13, 2023	DiversityGraph Generation	CodeCode Available	1
Generative Compositional Augmentations for Scene Graph Prediction	Jul 11, 2020	Graph GenerationLanguage Modelling	CodeCode Available	1
Graphical Contrastive Losses for Scene Graph Parsing	Mar 7, 2019	Relationship DetectionScene Graph Generation	CodeCode Available	1
Exploiting Edge-Oriented Reasoning for 3D Point-based Scene Graph Analysis	Mar 9, 2021	3d scene graph generationgraph construction	CodeCode Available	1
A Fair Ranking and New Model for Panoptic Scene Graph Generation	Jul 12, 2024	Graph GenerationPanoptic Scene Graph Generation	CodeCode Available	1
GeneAnnotator: A Semi-automatic Annotation Tool for Visual Scene Graph	Sep 6, 2021	Graph GenerationGraph Learning	CodeCode Available	1
GPS-Net: Graph Property Sensing Network for Scene Graph Generation	Mar 29, 2020	Graph GenerationScene Graph Generation	CodeCode Available	1
Expanding Scene Graph Boundaries: Fully Open-vocabulary Scene Graph Generation via Visual-Concept Alignment and Retention	Nov 18, 2023	Concept AlignmentGraph Generation	CodeCode Available	1

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All datasets Visual Genome 4D-OR 3R-Scan VRD GQA MM-OR MS-COCO

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	ExpressiveSGG	R@100	39.12	—	Unverified
2	NeuSyRE	R@100	39.1	—	Unverified
3	KnowZRel	zR@100	35.65	—	Unverified
4	SpeaQ (without reweighting)	Recall@50	32.9	—	Unverified
5	SpeaQ (with reweighting)	Recall@50	32.1	—	Unverified
6	Causal-TDE	Recall@50	31.93	—	Unverified
7	SG-EBM	Recall@50	31.74	—	Unverified
8	GPS-Net	Recall@50	28.9	—	Unverified
9	LOGIN	Recall@50	28.2	—	Unverified
10	VCTree	Recall@50	27.9	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ORacle	F1	0.91	—	Unverified
2	MM2SG	F1	0.9	—	Unverified
3	Pix2SG	F1	0.9	—	Unverified
4	LABRAD-OR	F1	0.88	—	Unverified
5	4D-OR baseline	F1	0.75	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	SceneGraphFusion	Top-5 Accuracy	0.87	—	Unverified
2	3DSSG [Wald2020_3dssg]	Top-5 Accuracy	0.66	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	FactorizableNet	Recall@50	18.32	—	Unverified
2	VRD	Recall@50	18.16	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	KnowZRel	zR@100	29.56	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	MM2SG	Macro F1	0.53	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	NeuSyRE	R@100	38.5	—	Unverified