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 141–150 of 483 papers

TitleStatusHype
Dynamic-Group-Aware Networks for Multi-Agent Trajectory Prediction with Relational ReasoningCode1
Set Interdependence Transformer: Set-to-Sequence Neural Networks for Permutation Learning and Structure Prediction—0
Relation Matters: Foreground-aware Graph-based Relational Reasoning for Domain Adaptive Object Detection—0
Subverting machines, fluctuating identities: Re-learning human categorization—0
Learning Meta Representations of One-shot Relations for Temporal Knowledge Graph Link Prediction—0
Leveraging Relational Information for Learning Weakly Disentangled RepresentationsCode0
R5: Rule Discovery with Reinforced and Recurrent Relational ReasoningCode0
Deep Inductive Logic Reasoning for Multi-Hop Reading Comprehension—0
Learning Reasoning Patterns for Relational Triple Extraction with Mutual Generation of Text and Graph—0
GroupNet: Multiscale Hypergraph Neural Networks for Trajectory Prediction with Relational ReasoningCode1
Show:102550
← PrevPage 15 of 49Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99—Unverified