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 171–180 of 483 papers

TitleStatusHype
Generalization of CNNs on Relational Reasoning with Bar ChartsCode0
Identifying Morality Frames in Political Tweets using Relational LearningCode0
Graph Neural Networks with Generated Parameters for Relation ExtractionCode0
Propagation on Multi-relational Graphs for Node RegressionCode0
Know-Evolve: Deep Temporal Reasoning for Dynamic Knowledge GraphsCode0
LightPath: Lightweight and Scalable Path Representation LearningCode0
OCRT: Boosting Foundation Models in the Open World with Object-Concept-Relation TriadCode0
Relation Network for Multi-label Aerial Image ClassificationCode0
Efficient Inference and Learning in a Large Knowledge Base: Reasoning with Extracted Information using a Locally Groundable First-Order Probabilistic Logic—0
Efficient Coarse-to-Fine Non-Local Module for the Detection of Small Objects—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99—Unverified