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
Context-Aware Scene Graph Generation With Seq2Seq TransformersCode1
Grounding Consistency: Distilling Spatial Common Sense for Precise Visual Relationship DetectionCode1
Efficient Initial Pose-graph Generation for Global SfMCode1
Neural Language Modeling for Contextualized Temporal Graph GenerationCode1
Dirichlet Graph Variational AutoencoderCode1
Dense Relational Image Captioning via Multi-task Triple-Stream NetworksCode1
Are scene graphs good enough to improve Image Captioning?Code1
Topology-Aware Generative Adversarial Network for Joint Prediction of Multiple Brain Graphs from a Single Brain GraphCode1
CogTree: Cognition Tree Loss for Unbiased Scene Graph GenerationCode1
PCPL: Predicate-Correlation Perception Learning for Unbiased Scene Graph GenerationCode1
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Benchmark Results

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