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 21–30 of 712 papers

TitleStatusHype
MolScribe: Robust Molecular Structure Recognition with Image-To-Graph GenerationCode2
FastFlows: Flow-Based Models for Molecular Graph GenerationCode2
RelTR: Relation Transformer for Scene Graph GenerationCode2
Unbiased Scene Graph Generation from Biased TrainingCode2
Learning to Compose Dynamic Tree Structures for Visual ContextsCode2
EgoExOR: An Ego-Exo-Centric Operating Room Dataset for Surgical Activity UnderstandingCode1
Directed Graph Grammars for Sequence-based LearningCode1
Fine-Grained Evaluation of Large Vision-Language Models in Autonomous DrivingCode1
DIFFVSGG: Diffusion-Driven Online Video Scene Graph GenerationCode1
Scale-Free Graph-Language ModelsCode1
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Benchmark Results

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