SOTAVerified

Graph Generation

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

Source: Graph Deconvolutional Generation

Papers

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Showing 171–180 of 712 papers

TitleStatusHype
Expressive Scene Graph Generation Using Commonsense Knowledge Infusion for Visual Understanding and ReasoningCode1
Learning Visual Commonsense for Robust Scene Graph GenerationCode1
Leveraging Predicate and Triplet Learning for Scene Graph GenerationCode1
Linguistic Structures as Weak Supervision for Visual Scene Graph GenerationCode1
Any-Property-Conditional Molecule Generation with Self-Criticism using Spanning TreesCode1
Expanding Scene Graph Boundaries: Fully Open-vocabulary Scene Graph Generation via Visual-Concept Alignment and RetentionCode1
ExplaGraphs: An Explanation Graph Generation Task for Structured Commonsense ReasoningCode1
MolHF: A Hierarchical Normalizing Flow for Molecular Graph GenerationCode1
Adaptive Graph Convolutional Recurrent Network for Traffic ForecastingCode1
Junction Tree Variational Autoencoder for Molecular Graph GenerationCode1
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Benchmark Results

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