Since Multimodal Emotion Recognition in Conversation (MERC) can be applied to public
opinion monitoring, intelligent dialogue robots, and other fields, it has received extensive …

Abstract Graph Neural Networks (GNNs) have received extensive research attention due to
their powerful information aggregation capabilities. Despite the success of GNNs, most of …

Graph contrastive learning has been successfully applied in text classification due to its
remarkable ability for self-supervised node representation learning. However, explicit graph …

Graph Contrastive Learning (GCL) excels at managing noise and fluctuations in input data,
making it popular in various fields (eg, social networks, and knowledge graphs). Our study …

Graph contrastive learning (GCL) has been widely applied to text classification tasks due to
its ability to generate self-supervised signals from unlabeled data, thus facilitating model …

Multi-modal entity alignment (MMEA) is essential for enhancing knowledge graphs and
improving information retrieval and question-answering systems. Existing methods often …

Remote sensing segmentation are widely applied in environmental protection, and urban
change detection, etc. Despite the success of deep learning-based remote sensing …

Abstract Multimodal Emotion Recognition in Conversations (MERC) aims to classify
utterance emotions using textual, auditory, and visual modal features. Most existing MERC …

In recent years, histopathological whole slide image (WSI)-based survival analysis has
attracted much attention in medical image analysis. In practice, WSIs usually come from …

Entity alignment is crucial for merging knowledge across knowledge graphs, as it matches
entities with identical semantics. The standard method matches these entities based on their …