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 131–140 of 483 papers

TitleStatusHype
Disentangling and Integrating Relational and Sensory Information in Transformer ArchitecturesCode0
Zero-Shot Relational Learning for Multimodal Knowledge GraphsCode0
Skews in the Phenomenon Space Hinder Generalization in Text-to-Image GenerationCode0
TRIP: Temporal Residual Learning with Image Noise Prior for Image-to-Video Diffusion Models—0
Towards Human-Like Machine Comprehension: Few-Shot Relational Learning in Visually-Rich Documents—0
SeCG: Semantic-Enhanced 3D Visual Grounding via Cross-modal Graph AttentionCode0
Deep Generative Models for Ultra-High Granularity Particle Physics Detector Simulation: A Voyage From Emulation to Extrapolation—0
Boosting gets full Attention for Relational Learning—0
Visual Reasoning in Object-Centric Deep Neural Networks: A Comparative Cognition ApproachCode0
FGeo-HyperGNet: Geometric Problem Solving Integrating Formal Symbolic System and Hypergraph Neural Network—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99—Unverified