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 11–20 of 483 papers

TitleStatusHype
BayReL: Bayesian Relational Learning for Multi-omics Data IntegrationCode1
Beyond Graph Neural Networks with Lifted Relational Neural NetworksCode1
3D Interaction Geometric Pre-training for Molecular Relational LearningCode1
Dynamic-Group-Aware Networks for Multi-Agent Trajectory Prediction with Relational ReasoningCode1
Abstractors and relational cross-attention: An inductive bias for explicit relational reasoning in TransformersCode1
Cross-Modal Causal Relational Reasoning for Event-Level Visual Question AnsweringCode1
DARER: Dual-task Temporal Relational Recurrent Reasoning Network for Joint Dialog Sentiment Classification and Act RecognitionCode1
Computer-aided Tuberculosis Diagnosis with Attribute Reasoning AssistanceCode1
COMPOSER: Compositional Reasoning of Group Activity in Videos with Keypoint-Only ModalityCode1
Conditional Graph Information Bottleneck for Molecular Relational LearningCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99—Unverified