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 221–230 of 483 papers

TitleStatusHype
HR-RCNN: Hierarchical Relational Reasoning for Object Detection—0
Pre and Post Counting for Scalable Statistical-Relational Model Discovery—0
Relational Neural Markov Random Fields—0
Propagation on Multi-relational Graphs for Node RegressionCode0
Recent Advances in Heterogeneous Relation Learning for Recommendation—0
An Insect-Inspired Randomly, Weighted Neural Network with Random Fourier Features For Neuro-Symbolic Relational LearningCode0
Identifying Morality Frames in Political Tweets using Relational LearningCode0
Reasoning Graph Networks for Kinship Verification: from Star-shaped to Hierarchical—0
Desk Organization: Effect of Multimodal Inputs on Spatial Relational Learning—0
Multi-Scale Progressive Attention Network for Video Question Answering—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99—Unverified