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
Doubly Reparameterized Importance Weighted Structure Learning for Scene Graph Generation—0
GraphMapper: Efficient Visual Navigation by Scene Graph Generation—0
Importance Weighted Structure Learning for Scene Graph Generation—0
Supplementing Missing Visions via Dialog for Scene Graph GenerationsCode0
Hierarchical Memory Learning for Fine-Grained Scene Graph Generation—0
Dual Embodied-Symbolic Concept Representations for Deep Learning—0
Commonsense Reasoning for Identifying and Understanding the Implicit Need of Help and Synthesizing Assistive Actions—0
Relation Regularized Scene Graph Generation—0
OG-SGG: Ontology-Guided Scene Graph Generation. A Case Study in Transfer Learning for Telepresence RoboticsCode0
Hyper-relationship Learning Network for Scene Graph Generation—0
Constrained Structure Learning for Scene Graph Generation—0
Scene Graph Generation: A Comprehensive Survey—0
Dynamic Scene Graph Generation via Anticipatory Pre-Training—0
PPDL: Predicate Probability Distribution Based Loss for Unbiased Scene Graph Generation—0
Exploiting Long-Term Dependencies for Generating Dynamic Scene GraphsCode0
Neural Belief Propagation for Scene Graph Generation—0
Joint Modeling of Visual Objects and Relations for Scene Graph Generation—0
Not All Relations are Equal: Mining Informative Labels for Scene Graph Generation—0
Scene Graph Generation with Geometric Context—0
SGTR: Generating Scene Graph by Learning Compositional Triplets with Transformer—0
Scene Graph Generation for Better Image Captioning?—0
BGT-Net: Bidirectional GRU Transformer Network for Scene Graph Generation—0
Learning of Visual Relations: The Devil is in the Tails—0
Semantic Compositional Learning for Low-shot Scene Graph Generation—0
Unconditional Scene Graph Generation—0
ReFormer: The Relational Transformer for Image CaptioningCode0
Image Scene Graph Generation (SGG) BenchmarkCode0
Predicate correlation learning for scene graph generation—0
Survey of Visual-Semantic Embedding Methods for Zero-Shot Image Retrieval—0
Segmentation-grounded Scene Graph Generation—0
Understanding the Role of Scene Graphs in Visual Question Answering—0
Counterfactual Thinking for Long-tailed Information Extraction—0
Topic Scene Graph Generation by Attention Distillation From Caption—0
A Simple Baseline for Weakly-Supervised Scene Graph Generation—0
Self-Supervised Real-to-Sim Scene Generation—0
Bridging Scene Understanding and Task Execution with Flexible Simulation Environments—0
Dual ResGCN for Balanced Scene GraphGeneration—0
After All, Only The Last Neuron Matters: Comparing Multi-modal Fusion Functions for Scene Graph GenerationCode0
Sim2SG: Sim-to-Real Scene Graph Generation for Transfer Learning—0
Exploring the Hierarchy in Relation Labels for Scene Graph Generation—0
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
Neuro-Symbolic Visual Reasoning: Disentangling "Visual" from "Reasoning"—0
Learning from the Scene and Borrowing from the Rich: Tackling the Long Tail in Scene Graph Generation—0
Visual Relationship Detection using Scene Graphs: A Survey—0
Unbiased Scene Graph Generation via Rich and Fair Semantic Extraction—0
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
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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