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 331–340 of 483 papers

TitleStatusHype
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
Cognitive Knowledge Graph Reasoning for One-shot Relational LearningCode0
WikiDataSets: Standardized sub-graphs from WikidataCode0
Relational Reasoning using Prior Knowledge for Visual Captioning—0
Adaptive Convolution for Multi-Relational Learning—0
Composition of Sentence Embeddings: Lessons from Statistical Relational Learning—0
Structural Relational Reasoning of Point Clouds—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99—Unverified