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
COMPOSER: Compositional Reasoning of Group Activity in Videos with Keypoint-Only ModalityCode1
Global-Reasoned Multi-Task Learning Model for Surgical Scene UnderstandingCode1
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
Cross-Modal Causal Relational Reasoning for Event-Level Visual Question AnsweringCode1
DualVGR: A Dual-Visual Graph Reasoning Unit for Video Question AnsweringCode1
EarthVQA: Towards Queryable Earth via Relational Reasoning-Based Remote Sensing Visual Question AnsweringCode1
BayReL: Bayesian Relational Learning for Multi-omics Data IntegrationCode1
Inductive Relation Prediction by Subgraph ReasoningCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99—Unverified