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 191–200 of 712 papers

TitleStatusHype
ORacle: Large Vision-Language Models for Knowledge-Guided Holistic OR Domain ModelingCode1
Expressive Scene Graph Generation Using Commonsense Knowledge Infusion for Visual Understanding and ReasoningCode1
Compositional Feature Augmentation for Unbiased Scene Graph GenerationCode1
Face Super-Resolution Using Stochastic Differential EquationsCode1
A Review and Efficient Implementation of Scene Graph Generation MetricsCode1
Knowledge Graph Generation From TextCode1
Permutation Invariant Graph Generation via Score-Based Generative ModelingCode1
LANDMARK: Language-guided Representation Enhancement Framework for Scene Graph GenerationCode1
SGTR: End-to-end Scene Graph Generation with TransformerCode1
Visual Graphs from Motion (VGfM): Scene understanding with object geometry reasoningCode1
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Benchmark Results

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