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 281–290 of 483 papers

TitleStatusHype
Search Space Properties for Learning a Class of Constraint-based Grammars—0
Self-supervised Multi-actor Social Activity Understanding in Streaming Videos—0
Set Interdependence Transformer: Set-to-Sequence Neural Networks for Permutation Learning and Structure Prediction—0
Shifting the Human-AI Relationship: Toward a Dynamic Relational Learning-Partner Model—0
Skeleton-based Relational Reasoning for Group Activity Analysis—0
SkILL - a Stochastic Inductive Logic Learner—0
Slice Normalized Dynamic Markov Logic Networks—0
Social-WaGDAT: Interaction-aware Trajectory Prediction via Wasserstein Graph Double-Attention Network—0
SOLD: Slot Object-Centric Latent Dynamics Models for Relational Manipulation Learning from Pixels—0
Solving Raven's Progressive Matrices with Multi-Layer Relation Networks—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99—Unverified