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 41–50 of 483 papers

TitleStatusHype
Compensating Supervision Incompleteness with Prior Knowledge in Semantic Image InterpretationCode1
CLUTRR: A Diagnostic Benchmark for Inductive Reasoning from TextCode1
Abstractors and relational cross-attention: An inductive bias for explicit relational reasoning in TransformersCode1
Generative 3D Part Assembly via Dynamic Graph LearningCode1
Global-Reasoned Multi-Task Learning Model for Surgical Scene UnderstandingCode1
CORE-Text: Improving Scene Text Detection with Contrastive Relational ReasoningCode1
Inductive Relation Prediction by BERTCode1
Inductive Relation Prediction by Subgraph ReasoningCode1
BayReL: Bayesian Relational Learning for Multi-omics Data IntegrationCode1
Enhancing the Utility of Higher-Order Information in Relational LearningCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99—Unverified