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 441–450 of 483 papers

TitleStatusHype
Object-Oriented Dynamics Learning through Multi-Level AbstractionCode0
Temporal Relational Reasoning in VideosCode0
OCRT: Boosting Foundation Models in the Open World with Object-Concept-Relation TriadCode0
A Comparative Study of Distributional and Symbolic Paradigms for Relational LearningCode0
One-Shot Relational Learning for Knowledge GraphsCode0
On Inductive Abilities of Latent Factor Models for Relational LearningCode0
Representing Prior Knowledge Using Randomly, Weighted Feature Networks for Visual Relationship DetectionCode0
RESOLVE: Relational Reasoning with Symbolic and Object-Level Features Using Vector Symbolic ProcessingCode0
Spatiotemporal Covariance Neural NetworksCode0
Open-Ended Multi-Modal Relational Reasoning for Video Question AnsweringCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99—Unverified