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 51–60 of 483 papers

TitleStatusHype
Relational Learning in Pre-Trained Models: A Theory from Hypergraph Recovery Perspective—0
Learning positional encodings in transformers depends on initialization—0
Disentangling and Integrating Relational and Sensory Information in Transformer ArchitecturesCode0
MLPs Learn In-Context on Regression and Classification TasksCode1
Zero-Shot Relational Learning for Multimodal Knowledge GraphsCode0
TRIP: Temporal Residual Learning with Image Noise Prior for Image-to-Video Diffusion Models—0
Skews in the Phenomenon Space Hinder Generalization in Text-to-Image GenerationCode0
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
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99—Unverified