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 401–410 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
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Benchmark Results

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