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Cross-modal Contrastive Attention Model for Medical Report Generation
Medical report automatic generation has gained increasing interest recently as a way to help radiologists write reports more efficiently.
Supervised Contrastive Learning with Structure Inference for Graph Classification
In this paper, we propose a novel graph neural network based on supervised contrastive learning with structure inference for graph classification.
Multi-task Self-distillation for Graph-based Semi-Supervised Learning
In this paper, we propose a multi-task self-distillation framework that injects self-supervised learning and self-distillation into graph convolutional networks to separately address the mismatch problem from the structure side and the label side.
Deep attributed graph clustering with self-separation regularization and parameter-free cluster estimation
Modern graph neural networks provide facilitation to jointly capture the above information in attributed graphs with a feature aggregation manner, and have achieved great success in attributed graph clustering.
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