SOTAVerified

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 AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 251–300 of 318 papers

TitleStatusHype
Segmentation-grounded Scene Graph Generation—0
Bipartite Graph Network with Adaptive Message Passing for Unbiased Scene Graph GenerationCode1
Fully Convolutional Scene Graph GenerationCode1
Visual Distant Supervision for Scene Graph GenerationCode1
SceneGraphFusion: Incremental 3D Scene Graph Prediction from RGB-D SequencesCode1
Probabilistic Modeling of Semantic Ambiguity for Scene Graph GenerationCode1
Exploiting Edge-Oriented Reasoning for 3D Point-based Scene Graph AnalysisCode1
Energy-Based Learning for Scene Graph GenerationCode1
Understanding the Role of Scene Graphs in Visual Question Answering—0
Context-Aware Scene Graph Generation With Seq2Seq TransformersCode1
Topic Scene Graph Generation by Attention Distillation From Caption—0
A Simple Baseline for Weakly-Supervised Scene Graph Generation—0
Counterfactual Thinking for Long-tailed Information Extraction—0
Self-Supervised Real-to-Sim Scene Generation—0
Bridging Scene Understanding and Task Execution with Flexible Simulation Environments—0
After All, Only The Last Neuron Matters: Comparing Multi-modal Fusion Functions for Scene Graph GenerationCode0
Dual ResGCN for Balanced Scene GraphGeneration—0
Dense Relational Image Captioning via Multi-task Triple-Stream NetworksCode1
Sim2SG: Sim-to-Real Scene Graph Generation for Transfer Learning—0
Are scene graphs good enough to improve Image Captioning?Code1
CogTree: Cognition Tree Loss for Unbiased Scene Graph GenerationCode1
Exploring the Hierarchy in Relation Labels for Scene Graph Generation—0
PCPL: Predicate-Correlation Perception Learning for Unbiased Scene Graph GenerationCode1
Tackling the Unannotated: Scene Graph Generation with Bias-Reduced Models—0
HOSE-Net: Higher Order Structure Embedded Network for Scene Graph Generation—0
Assisting Scene Graph Generation with Self-Supervision—0
Sketching Image Gist: Human-Mimetic Hierarchical Scene Graph GenerationCode1
Generative Compositional Augmentations for Scene Graph PredictionCode1
Learning and Reasoning with the Graph Structure Representation in Robotic SurgeryCode1
Neuro-Symbolic Visual Reasoning: Disentangling "Visual" from "Reasoning"—0
Learning Visual Commonsense for Robust Scene Graph GenerationCode1
Learning from the Scene and Borrowing from the Rich: Tackling the Long Tail in Scene Graph Generation—0
Graph Density-Aware Losses for Novel Compositions in Scene Graph GenerationCode1
Visual Relationship Detection using Scene Graphs: A Survey—0
Relation Transformer NetworkCode1
GPS-Net: Graph Property Sensing Network for Scene Graph GenerationCode1
Unbiased Scene Graph Generation from Biased TrainingCode2
Unbiased Scene Graph Generation via Rich and Fair Semantic Extraction—0
NODIS: Neural Ordinary Differential Scene UnderstandingCode1
Weakly Supervised Visual Semantic ParsingCode1
Bridging Knowledge Graphs to Generate Scene GraphsCode1
Leveraging Auxiliary Text for Deep Recognition of Unseen Visual Relationships—0
The Limited Multi-Label Projection LayerCode0
Learning Predicates as Functions to Enable Few-shot Scene Graph Prediction—0
Exploring Context and Visual Pattern of Relationship for Scene Graph Generation—0
Contextual Translation Embedding for Visual Relationship Detection and Scene Graph Generation—0
Target-Tailored Source-Transformation for Scene Graph Generation—0
Scene Graph Generation with External Knowledge and Image Reconstruction—0
Visual Semantic Information Pursuit: A Survey—0
Knowledge-Embedded Routing Network for Scene Graph GenerationCode1
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All datasetsVisual Genome4D-OR3R-ScanVRDGQAMM-ORMS-COCO

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ExpressiveSGGR@10039.12—Unverified
2NeuSyRER@10039.1—Unverified
3KnowZRelzR@10035.65—Unverified
4SpeaQ (without reweighting)Recall@5032.9—Unverified
5SpeaQ (with reweighting)Recall@5032.1—Unverified
6Causal-TDERecall@5031.93—Unverified
7SG-EBMRecall@5031.74—Unverified
8GPS-NetRecall@5028.9—Unverified
9LOGINRecall@5028.2—Unverified
10VCTreeRecall@5027.9—Unverified
#ModelMetricClaimedVerifiedStatus
1ORacleF10.91—Unverified
2MM2SGF10.9—Unverified
3Pix2SGF10.9—Unverified
4LABRAD-ORF10.88—Unverified
54D-OR baselineF10.75—Unverified
#ModelMetricClaimedVerifiedStatus
1SceneGraphFusionTop-5 Accuracy0.87—Unverified
23DSSG [Wald2020_3dssg]Top-5 Accuracy0.66—Unverified
#ModelMetricClaimedVerifiedStatus
1FactorizableNetRecall@5018.32—Unverified
2VRDRecall@5018.16—Unverified
#ModelMetricClaimedVerifiedStatus
1KnowZRelzR@10029.56—Unverified
#ModelMetricClaimedVerifiedStatus
1MM2SGMacro F10.53—Unverified
#ModelMetricClaimedVerifiedStatus
1NeuSyRER@10038.5—Unverified