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 181–190 of 483 papers

TitleStatusHype
Propagation on Multi-relational Graphs for Node RegressionCode0
Recent Advances in Heterogeneous Relation Learning for Recommendation—0
Relation Prediction as an Auxiliary Training Objective for Improving Multi-Relational Graph RepresentationsCode1
An Insect-Inspired Randomly, Weighted Neural Network with Random Fourier Features For Neuro-Symbolic Relational LearningCode0
Identifying Morality Frames in Political Tweets using Relational LearningCode0
Reasoning Graph Networks for Kinship Verification: from Star-shaped to Hierarchical—0
Desk Organization: Effect of Multimodal Inputs on Spatial Relational Learning—0
Probing Cross-Modal Representations in Multi-Step Relational ReasoningCode0
Learning to Solve NLP Tasks in an Incremental Number of Languages—0
Multi-Scale Progressive Attention Network for Video Question Answering—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99—Unverified