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 31–40 of 712 papers

TitleStatusHype
Generalized Visual Relation Detection with Diffusion Models—0
HBS -- Hardware Build System: A Tcl-based, minimal common abstraction approach for build system for hardware designsCode0
SAMJAM: Zero-Shot Video Scene Graph Generation for Egocentric Kitchen Videos—0
Beyond LLMs: A Linguistic Approach to Causal Graph Generation from Narrative Texts—0
NuScenes-SpatialQA: A Spatial Understanding and Reasoning Benchmark for Vision-Language Models in Autonomous Driving—0
Make Autoregressive Great Again: Diffusion-Free Graph Generation with Next-Scale Prediction—0
A Semantic-Enhanced Heterogeneous Graph Learning Method for Flexible Objects Recognition—0
Critical Iterative Denoising: A Discrete Generative Model Applied to Graphs—0
Fine-Grained Evaluation of Large Vision-Language Models in Autonomous DrivingCode1
A Causal Adjustment Module for Debiasing 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