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 376–400 of 712 papers

TitleStatusHype
Understanding the Role of Scene Graphs in Visual Question Answering—0
Contextual Translation Embedding for Visual Relationship Detection and Scene Graph Generation—0
UniQ: Unified Decoder with Task-specific Queries for Efficient Scene Graph Generation—0
Universal Scene Graph Generation—0
Unsupervised Task Graph Generation from Instructional Video Transcripts—0
Using Retriever Augmented Large Language Models for Attack Graph Generation—0
Utilizing Graph Generation for Enhanced Domain Adaptive Object Detection—0
Variational Flow Matching for Graph Generation—0
Discrete Graph Auto-Encoder—0
VersaGNN: a Versatile accelerator for Graph neural networks—0
Visual Relationship Detection using Scene Graphs: A Survey—0
Learning Predicates as Functions to Enable Few-shot Scene Graph Prediction—0
Visual Semantic Information Pursuit: A Survey—0
Visual Traffic Knowledge Graph Generation from Scene Images—0
What can Off-the-Shelves Large Multi-Modal Models do for Dynamic Scene Graph Generation?—0
Will More Expressive Graph Neural Networks do Better on Generative Tasks?—0
Brain Imaging-to-Graph Generation using Adversarial Hierarchical Diffusion Models for MCI Causality Analysis—0
3D Scene Graph Prediction on Point Clouds Using Knowledge Graphs—0
A Causal Adjustment Module for Debiasing Scene Graph Generation—0
Accelerating Medical Knowledge Discovery through Automated Knowledge Graph Generation and Enrichment—0
Accurate polyglot semantic parsing with DAG grammars—0
Adaptive Fine-Grained Predicates Learning for Scene Graph Generation—0
Agentic Medical Knowledge Graphs Enhance Medical Question Answering: Bridging the Gap Between LLMs and Evolving Medical Knowledge—0
Adversarial Stein Training for Graph Energy Models—0
Adversarial Stein Training for Graph Energy Models—0
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Benchmark Results

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