SOTAVerified

Relational Reasoning

The goal of Relational Reasoning is to figure out the relationships among different entities, such as image pixels, words or sentences, human skeletons or interactive moving agents.

Source: Social-WaGDAT: Interaction-aware Trajectory Prediction via Wasserstein Graph Double-Attention Network

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 61–70 of 483 papers

TitleStatusHype
Boosting gets full Attention for Relational Learning—0
Visual Reasoning in Object-Centric Deep Neural Networks: A Comparative Cognition ApproachCode0
FGeo-HyperGNet: Geometric Problem Solving Integrating Formal Symbolic System and Hypergraph Neural Network—0
Position: Topological Deep Learning is the New Frontier for Relational Learning—0
Pix2Code: Learning to Compose Neural Visual Concepts as ProgramsCode1
MolTC: Towards Molecular Relational Modeling In Language ModelsCode2
Multi-Agent Dynamic Relational Reasoning for Social Robot Navigation—0
LLMs for Relational Reasoning: How Far are We?—0
MLAD: A Unified Model for Multi-system Log Anomaly Detection—0
Higher-order Relational Reasoning for Pedestrian Trajectory Prediction—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99—Unverified