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 151–175 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
Large Class Separation is not what you need for Relational Reasoning-based OOD DetectionCode0
A Multi-Task Perspective for Link Prediction with New Relation Types and Nodes—0
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
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99—Unverified