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 421–430 of 712 papers

TitleStatusHype
Environment-Invariant Curriculum Relation Learning for Fine-Grained Scene Graph GenerationCode0
Improving Scene Graph Generation with Superpixel-Based Interaction Learning—0
Text2KGBench: A Benchmark for Ontology-Driven Knowledge Graph Generation from TextCode0
Interpretable End-to-End Driving Model for Implicit Scene Understanding—0
Triple Correlations-Guided Label Supplementation for Unbiased Video Scene Graph Generation—0
Addressing the Impact of Localized Training Data in Graph Neural NetworksCode0
Pixel-wise Graph Attention Networks for Person Re-identificationCode0
Disentangling Node Attributes from Graph Topology for Improved Generalizability in Link Prediction—0
Unbiased Scene Graph Generation via Two-stage Causal Modeling—0
Open-Vocabulary Object Detection via Scene Graph Discovery—0
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Benchmark Results

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