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 331–340 of 483 papers

TitleStatusHype
RuDaS: Synthetic Datasets for Rule Learning and Evaluation ToolsCode0
PARN: Position-Aware Relation Networks for Few-Shot LearningCode0
Relationships from Entity StreamCode0
Meta Relational Learning for Few-Shot Link Prediction in Knowledge GraphsCode0
The Impact of Semantic Linguistic Features in Relation Extraction: A Logical Relational Learning Approach—0
Hyperlink Regression via Bregman Divergence—0
Improving Skip-Gram based Graph Embeddings via Centrality-Weighted Sampling—0
Relation Network for Multi-label Aerial Image ClassificationCode0
End-to-end Recurrent Multi-Object Tracking and Trajectory Prediction with Relational Reasoning—0
Collaborative Metric Learning with Memory Network for Multi-Relational Recommender Systems—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99—Unverified