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 321–330 of 483 papers

TitleStatusHype
Enhancing Large Language Models with Domain-Specific Knowledge: The Case in Topological Materials—0
Towards Abstract Relational Learning in Human Robot Interaction—0
Towards Human-Like Machine Comprehension: Few-Shot Relational Learning in Visually-Rich Documents—0
Towards Omni-Supervised Face Alignment for Large Scale Unlabeled Videos—0
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
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99—Unverified