SOTAVerified

Graph Generation

Graph Generation is an important research area with significant applications in drug and material designs.

Source: Graph Deconvolutional Generation

Papers

Showing 150 of 712 papers

TitleStatusHype
4D Panoptic Scene Graph GenerationCode3
Learning to Compose Dynamic Tree Structures for Visual ContextsCode2
GDGB: A Benchmark for Generative Dynamic Text-Attributed Graph LearningCode2
SkySenseGPT: A Fine-Grained Instruction Tuning Dataset and Model for Remote Sensing Vision-Language UnderstandingCode2
Generative Diffusion Models on Graphs: Methods and ApplicationsCode2
From Pixels to Graphs: Open-Vocabulary Scene Graph Generation with Vision-Language ModelsCode2
Graph Condensation: A SurveyCode2
MolScribe: Robust Molecular Structure Recognition with Image-To-Graph GenerationCode2
DiscoSG: Towards Discourse-Level Text Scene Graph Parsing through Iterative Graph RefinementCode2
DeFoG: Discrete Flow Matching for Graph GenerationCode2
REACT: Real-time Efficiency and Accuracy Compromise for Tradeoffs in Scene Graph GenerationCode2
Boosting Knowledge Graph Generation from Tabular Data with RML ViewsCode2
EGTR: Extracting Graph from Transformer for Scene Graph GenerationCode2
FastFlows: Flow-Based Models for Molecular Graph GenerationCode2
STAR: A First-Ever Dataset and A Large-Scale Benchmark for Scene Graph Generation in Large-Size Satellite ImageryCode2
Geometry-Complete Diffusion for 3D Molecule Generation and OptimizationCode2
Open World Scene Graph Generation using Vision Language ModelsCode2
RelTR: Relation Transformer for Scene Graph GenerationCode2
SGTR+: End-to-end Scene Graph Generation with TransformerCode2
Unbiased Scene Graph Generation from Biased TrainingCode2
LLM-Based Multi-Agent Systems are Scalable Graph Generative ModelsCode2
DiffGraph: Heterogeneous Graph Diffusion ModelCode2
DiGress: Discrete Denoising diffusion for graph generationCode2
RelationField: Relate Anything in Radiance FieldsCode2
MM-OR: A Large Multimodal Operating Room Dataset for Semantic Understanding of High-Intensity Surgical EnvironmentsCode2
Efficient Initial Pose-graph Generation for Global SfMCode1
Efficient and Scalable Graph Generation through Iterative Local ExpansionCode1
Efficient and Degree-Guided Graph Generation via Discrete Diffusion ModelingCode1
Efficient Graph Generation with Graph Recurrent Attention NetworksCode1
EgoExOR: An Ego-Exo-Centric Operating Room Dataset for Surgical Activity UnderstandingCode1
Energy-Based Learning for Scene Graph GenerationCode1
A Graph is Worth K Words: Euclideanizing Graph using Pure TransformerCode1
Adaptive Self-training Framework for Fine-grained Scene Graph GenerationCode1
Dual-branch Hybrid Learning Network for Unbiased Scene Graph GenerationCode1
Dirichlet Graph Variational AutoencoderCode1
3D Vessel Graph Generation Using Denoising DiffusionCode1
AutoKG: Efficient Automated Knowledge Graph Generation for Language ModelsCode1
Discrete-state Continuous-time Diffusion for Graph GenerationCode1
Expanding Scene Graph Boundaries: Fully Open-vocabulary Scene Graph Generation via Visual-Concept Alignment and RetentionCode1
Diffusion-based Graph Generative MethodsCode1
Adaptive Graph Convolutional Recurrent Network for Traffic ForecastingCode1
Any-Property-Conditional Molecule Generation with Self-Criticism using Spanning TreesCode1
DIFFVSGG: Diffusion-Driven Online Video Scene Graph GenerationCode1
Data Imputation with Iterative Graph ReconstructionCode1
A Fair Ranking and New Model for Panoptic Scene Graph GenerationCode1
Are scene graphs good enough to improve Image Captioning?Code1
Dense Relational Image Captioning via Multi-task Triple-Stream NetworksCode1
A Review and Efficient Implementation of Scene Graph Generation MetricsCode1
A Simple and Scalable Representation for Graph GenerationCode1
CycleGT: Unsupervised Graph-to-Text and Text-to-Graph Generation via Cycle TrainingCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1RNNStreetMover0.03Unverified
2GraphRNNStreetMover0.02Unverified
3GGT without CAStreetMover0.02Unverified
4GGTStreetMover0.02Unverified