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 471–480 of 483 papers

TitleStatusHype
LLMs for Relational Reasoning: How Far are We?—0
Locally Boosted Graph Aggregation for Community Detection—0
Local Region Perception and Relationship Learning Combined with Feature Fusion for Facial Action Unit Detection—0
Logistic Tensor Factorization for Multi-Relational Data—0
Look Around for Anomalies: Weakly-Supervised Anomaly Detection via Context-Motion Relational Learning—0
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
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99—Unverified