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 476–483 of 483 papers

TitleStatusHype
Matrix and Tensor Factorization Methods for Natural Language Processing—0
Medical Image Analysis using Deep Relational Learning—0
Metric-guided Distillation: Distilling Knowledge from the Metric to Ranker and Retriever for Generative Commonsense Reasoning—0
MGRR-Net: Multi-level Graph Relational Reasoning Network for Facial Action Units Detection—0
Mix and Reason: Reasoning over Semantic Topology with Data Mixing for Domain Generalization—0
Mixture-of-Graphs: Zero-shot Relational Learning for Knowledge Graph by Fusing Ontology and Textual Experts—0
MLAD: A Unified Model for Multi-system Log Anomaly Detection—0
Modelling Compositionality and Structure Dependence in Natural Language—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99—Unverified