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
DARER: Dual-task Temporal Relational Recurrent Reasoning Network for Joint Dialog Sentiment Classification and Act RecognitionCode1
Deep Relational Reasoning Graph Network for Arbitrary Shape Text DetectionCode1
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
Conditional Graph Information Bottleneck for Molecular Relational LearningCode1
CORE-Text: Improving Scene Text Detection with Contrastive Relational ReasoningCode1
Compensating Supervision Incompleteness with Prior Knowledge in Semantic Image InterpretationCode1
Computer-aided Tuberculosis Diagnosis with Attribute Reasoning AssistanceCode1
CLUTRR: A Diagnostic Benchmark for Inductive Reasoning from TextCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99—Unverified