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 31–40 of 483 papers

TitleStatusHype
Computer-aided Tuberculosis Diagnosis with Attribute Reasoning AssistanceCode1
Dynamic-Group-Aware Networks for Multi-Agent Trajectory Prediction with Relational ReasoningCode1
GroupNet: Multiscale Hypergraph Neural Networks for Trajectory Prediction with Relational ReasoningCode1
Learning to Reason Deductively: Math Word Problem Solving as Complex Relation ExtractionCode1
DARER: Dual-task Temporal Relational Recurrent Reasoning Network for Joint Dialog Sentiment Classification and Act RecognitionCode1
Global-Reasoned Multi-Task Learning Model for Surgical Scene UnderstandingCode1
CORE-Text: Improving Scene Text Detection with Contrastive Relational ReasoningCode1
COMPOSER: Compositional Reasoning of Group Activity in Videos with Keypoint-Only ModalityCode1
Systematic Generalization with Edge TransformersCode1
Topological Relational Learning on GraphsCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99—Unverified