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 101–110 of 483 papers

TitleStatusHype
Abstractors and relational cross-attention: An inductive bias for explicit relational reasoning in TransformersCode1
Medical Image Analysis using Deep Relational Learning—0
Your Diffusion Model is Secretly a Zero-Shot ClassifierCode2
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
Heterogeneous Graph Contrastive Learning for RecommendationCode1
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
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99—Unverified