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 41–50 of 483 papers

TitleStatusHype
Hypergraph-based Motion Generation with Multi-modal Interaction Relational Reasoning—0
Spatiotemporal Covariance Neural NetworksCode0
Enhancing Large Language Models with Domain-Specific Knowledge: The Case in Topological Materials—0
A Modern Take on Visual Relationship Reasoning for Grasp Planning—0
Genesis: Towards the Automation of Systems Biology Research—0
Battery GraphNets : Relational Learning for Lithium-ion Batteries(LiBs) Life Estimation—0
Beyond the Doors of Perception: Vision Transformers Represent Relations Between ObjectsCode0
Behavior-Inspired Neural Networks for Relational Inference—0
Self-supervised Multi-actor Social Activity Understanding in Streaming Videos—0
Few-shot Knowledge Graph Relational Reasoning via Subgraph AdaptationCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99—Unverified