SOTAVerified

Graph Generation

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

Source: Graph Deconvolutional Generation

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 91–100 of 712 papers

TitleStatusHype
Energy-Based Learning for Scene Graph GenerationCode1
Dirichlet Graph Variational AutoencoderCode1
Are scene graphs good enough to improve Image Captioning?Code1
Generative Compositional Augmentations for Scene Graph PredictionCode1
Directed Graph Grammars for Sequence-based LearningCode1
Compositional Feature Augmentation for Unbiased Scene Graph GenerationCode1
A Review and Efficient Implementation of Scene Graph Generation MetricsCode1
Conditional Diffusion Based on Discrete Graph Structures for Molecular Graph GenerationCode1
GraphGDP: Generative Diffusion Processes for Permutation Invariant Graph GenerationCode1
DIFFVSGG: Diffusion-Driven Online Video Scene Graph GenerationCode1
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Benchmark Results

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