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

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Showing 151–160 of 483 papers

TitleStatusHype
Optimal quadratic binding for relational reasoning in vector symbolic neural architecturesCode0
Relational Reasoning Over Spatial-Temporal Graphs for Video Summarization—0
Spatio-Temporal Dynamic Graph Relation Learning for Urban Metro Flow Prediction—0
MGRR-Net: Multi-level Graph Relational Reasoning Network for Facial Action Units Detection—0
Learning to Reason Deductively: Math Word Problem Solving as Complex Relation ExtractionCode1
MoReL: Multi-omics Relational Learning—0
DARER: Dual-task Temporal Relational Recurrent Reasoning Network for Joint Dialog Sentiment Classification and Act RecognitionCode1
Important Object Identification with Semi-Supervised Learning for Autonomous Driving—0
Probabilities of the Third Type: Statistical Relational Learning and Reasoning with Relative Frequencies—0
Global-Reasoned Multi-Task Learning Model for Surgical Scene UnderstandingCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99—Unverified