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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 126–150 of 483 papers

TitleStatusHype
Differentiable Parsing and Visual Grounding of Natural Language Instructions for Object Placement—0
Swift Markov Logic for Probabilistic Reasoning on Knowledge Graphs—0
Relational Reasoning via Set Transformers: Provable Efficiency and Applications to MARL—0
Structured Knowledge Grounding for Question Answering—0
VGStore: A Multimodal Extension to SPARQL for Querying RDF Scene GraphCode0
Hierarchical Relational Learning for Few-Shot Knowledge Graph Completion—0
Graph Convolutional Networks from the Perspective of Sheaves and the Neural Tangent Kernel—0
EvolveHypergraph: Group-Aware Dynamic Relational Reasoning for Trajectory Prediction—0
Cross-Modal Causal Relational Reasoning for Event-Level Visual Question AnsweringCode1
Semantic Novelty Detection via Relational ReasoningCode1
Sparse Relational Reasoning with Object-Centric Representations—0
Video Dialog as Conversation about Objects Living in Space-TimeCode1
3D Part Assembly Generation with Instance Encoded Transformer—0
Specializing Pre-trained Language Models for Better Relational Reasoning via Network PruningCode1
Computer-aided Tuberculosis Diagnosis with Attribute Reasoning AssistanceCode1
Dynamic-Group-Aware Networks for Multi-Agent Trajectory Prediction with Relational ReasoningCode1
Set Interdependence Transformer: Set-to-Sequence Neural Networks for Permutation Learning and Structure Prediction—0
Relation Matters: Foreground-aware Graph-based Relational Reasoning for Domain Adaptive Object Detection—0
Subverting machines, fluctuating identities: Re-learning human categorization—0
Learning Meta Representations of One-shot Relations for Temporal Knowledge Graph Link Prediction—0
Leveraging Relational Information for Learning Weakly Disentangled RepresentationsCode0
R5: Rule Discovery with Reinforced and Recurrent Relational ReasoningCode0
Deep Inductive Logic Reasoning for Multi-Hop Reading Comprehension—0
Learning Reasoning Patterns for Relational Triple Extraction with Mutual Generation of Text and Graph—0
GroupNet: Multiscale Hypergraph Neural Networks for Trajectory Prediction with Relational ReasoningCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99—Unverified