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Found 9 papers, 5 papers with code
Multi-view Graph Structural Representation Learning via Graph Coarsening
Specifically, we build three unique views, original, coarsening, and conversion, to learn a thorough structural representation.
In the Blink of an Eye: Event-based Emotion Recognition
To the best of our knowledge, our method is the first eye-based emotion recognition method that leverages event-based cameras and spiking neural network.
From Alignment to Entailment: A Unified Textual Entailment Framework for Entity Alignment
Our approach captures the unified correlation pattern of two kinds of information between entities, and explicitly models the fine-grained interaction between original entity information.
H2TNE: Temporal Heterogeneous Information Network Embedding in Hyperbolic Spaces
Temporal heterogeneous information network (temporal HIN) embedding, aiming to represent various types of nodes of different timestamps into low dimensional spaces while preserving structural and semantic information, is of vital importance in diverse real-life tasks.
HGWaveNet: A Hyperbolic Graph Neural Network for Temporal Link Prediction
In this paper, we propose HGWaveNet, a novel hyperbolic graph neural network that fully exploits the fitness between hyperbolic spaces and data distributions for temporal link prediction.
Consistent Depth Prediction for Transparent Object Reconstruction from RGB-D Camera
We compare the metrics on our dataset and SLAM reconstruction results in both synthetic scenes and real scenes with the previous methods.
MoSE: Modality Split and Ensemble for Multimodal Knowledge Graph Completion
Based on these embeddings, in the inference phase, we first make modality-split predictions and then exploit various ensemble methods to combine the predictions with different weights, which models the modality importance dynamically.
Spiking Transformers for Event-Based Single Object Tracking
In particular, the proposed architecture features a transformer module to provide global spatial information and a spiking neural network (SNN) module for extracting temporal cues.
PPDL: Predicate Probability Distribution Based Loss for Unbiased Scene Graph Generation
However, the application value of SG on downstream tasks is severely limited by the predicate classification bias, which is caused by long-tailed data and presented as semantic bias of predicted relation predicates.
