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 151–200 of 483 papers

TitleStatusHype
Lifted Inference beyond First-Order LogicCode0
Learning the meanings of function words from grounded language using a visual question answering modelCode0
CommonsenseVIS: Visualizing and Understanding Commonsense Reasoning Capabilities of Natural Language Models—0
LightPath: Lightweight and Scalable Path Representation LearningCode0
Anticipating Technical Expertise and Capability Evolution in Research Communities using Dynamic Graph TransformersCode0
A Multi-Task Perspective for Link Prediction with New Relation Types and Nodes—0
Large Class Separation is not what you need for Relational Reasoning-based OOD DetectionCode0
Statistical relational learning and neuro-symbolic AI: what does first-order logic offer?—0
In-Context Analogical Reasoning with Pre-Trained Language ModelsCode0
Modularized Zero-shot VQA with Pre-trained ModelsCode0
Continual Reasoning: Non-Monotonic Reasoning in Neurosymbolic AI using Continual Learning—0
Retrieval-based Knowledge Augmented Vision Language Pre-training—0
Cluster Flow: how a hierarchical clustering layer make allows deep-NNs more resilient to hacking, more human-like and easily implements relational reasoning—0
Geometric Relational Embeddings: A Survey—0
Medical Image Analysis using Deep Relational Learning—0
Enhancing Embedding Representations of Biomedical Data using Logic Knowledge—0
Local Region Perception and Relationship Learning Combined with Feature Fusion for Facial Action Unit Detection—0
Ultra-High-Resolution Detector Simulation with Intra-Event Aware GAN and Self-Supervised Relational ReasoningCode0
Weighted First Order Model Counting with Directed Acyclic Graph Axioms—0
Principled and Efficient Motif Finding for Structure Learning of Lifted Graphical ModelsCode0
DEVICE: DEpth and VIsual ConcEpts Aware Transformer for TextCaps—0
Double Equivariance for Inductive Link Prediction for Both New Nodes and New Relation TypesCode0
Knowledge-augmented Graph Neural Networks with Concept-aware Attention for Adverse Drug Event Detection—0
ReVoLT: Relational Reasoning and Voronoi Local Graph Planning for Target-driven Navigation—0
Look Around for Anomalies: Weakly-Supervised Anomaly Detection via Context-Motion Relational Learning—0
SrTR: Self-reasoning Transformer with Visual-linguistic Knowledge for Scene Graph Generation—0
Breakpoint Transformers for Modeling and Tracking Intermediate BeliefsCode0
Learning Probabilistic Temporal Safety Properties from Examples in Relational Domains—0
Lifted Inference with Linear Order Axiom—0
Metric-guided Distillation: Distilling Knowledge from the Metric to Ranker and Retriever for Generative Commonsense Reasoning—0
Mix and Reason: Reasoning over Semantic Topology with Data Mixing for Domain Generalization—0
Coresets for Relational Data and The ApplicationsCode0
Swift Markov Logic for Probabilistic Reasoning on Knowledge Graphs—0
Differentiable Parsing and Visual Grounding of Natural Language Instructions for Object Placement—0
Decoupling Mixture-of-Graphs: Unseen Relational Learning for Knowledge Graph Completion by Fusing Ontology and Textual Experts—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
Sparse Relational Reasoning with Object-Centric Representations—0
3D Part Assembly Generation with Instance Encoded Transformer—0
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
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99—Unverified