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 401–425 of 712 papers

TitleStatusHype
TextPSG: Panoptic Scene Graph Generation from Textual Descriptions—0
Domain-wise Invariant Learning for Panoptic Scene Graph Generation—0
Adaptive Visual Scene Understanding: Incremental Scene Graph GenerationCode0
Logical Bias Learning for Object Relation Prediction—0
Streamlining Attack Tree Generation: A Fragment-Based Approach—0
SANGEA: Scalable and Attributed Network Generation—0
Predicate Classification Using Optimal Transport Loss in Scene Graph Generation—0
Towards Debiasing Frame Length Bias in Text-Video Retrieval via Causal Intervention—0
STDG: Semi-Teacher-Student Training Paradigram for Depth-guided One-stage Scene Graph Generation—0
RepSGG: Novel Representations of Entities and Relationships for Scene Graph Generation—0
Haystack: A Panoptic Scene Graph Dataset to Evaluate Rare Predicate ClassesCode0
An Accurate Graph Generative Model with Tunable Features—0
Towards Addressing the Misalignment of Object Proposal Evaluation for Vision-Language Tasks via Semantic GroundingCode0
Head-Tail Cooperative Learning Network for Unbiased Scene Graph GenerationCode0
Will More Expressive Graph Neural Networks do Better on Generative Tasks?—0
RBA-GCN: Relational Bilevel Aggregation Graph Convolutional Network for Emotion RecognitionCode0
3D Scene Graph Prediction on Point Clouds Using Knowledge Graphs—0
Local-Global Information Interaction Debiasing for Dynamic Scene Graph Generation—0
Informative Scene Graph Generation via Debiasing—0
Generalized Unbiased Scene Graph Generation—0
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
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Benchmark Results

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