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 291–300 of 483 papers

TitleStatusHype
Transfer Learning in Visual and Relational Reasoning—0
TransGCN:Coupling Transformation Assumptions with Graph Convolutional Networks for Link Prediction—0
TransRev: Modeling Reviews as Translations from Users to Items—0
TRIP: Temporal Residual Learning with Image Noise Prior for Image-to-Video Diffusion Models—0
Understanding the Complexity of Lifted Inference and Asymmetric Weighted Model Counting—0
Unified Graph Structured Models for Video Understanding—0
User Profiling Using Hinge-loss Markov Random Fields—0
Variational Deep Logic Network for Joint Inference of Entities and Relations—0
Variational Quantum Circuit Model for Knowledge Graphs Embedding—0
VC-Dimension Based Generalization Bounds for Relational Learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99—Unverified