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 21–30 of 483 papers

TitleStatusHype
Relational Context Learning for Human-Object Interaction DetectionCode1
Abstractors and relational cross-attention: An inductive bias for explicit relational reasoning in TransformersCode1
Heterogeneous Graph Contrastive Learning for RecommendationCode1
IRRGN: An Implicit Relational Reasoning Graph Network for Multi-turn Response SelectionCode1
Few-shot Relational Reasoning via Connection Subgraph PretrainingCode1
Relational program synthesis with numerical reasoningCode1
Cross-Modal Causal Relational Reasoning for Event-Level Visual Question AnsweringCode1
Semantic Novelty Detection via Relational ReasoningCode1
Video Dialog as Conversation about Objects Living in Space-TimeCode1
Computer-aided Tuberculosis Diagnosis with Attribute Reasoning AssistanceCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99—Unverified