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 251–260 of 483 papers

TitleStatusHype
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
Sparse Relational Reasoning with Object-Centric Representations—0
Spatial Knowledge Distillation to aid Visual Reasoning—0
Spatially Invariant Unsupervised 3D Object-Centric Learning and Scene Decomposition—0
Spatio-Temporal Dynamic Graph Relation Learning for Urban Metro Flow Prediction—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99—Unverified