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A review of key technologies for emotion analysis using multimodal information
Emotion analysis, an integral aspect of human–machine interactions, has witnessed
significant advancements in recent years. With the rise of multimodal data sources such as …
significant advancements in recent years. With the rise of multimodal data sources such as …
Masked graph learning with recurrent alignment for multimodal emotion recognition in conversation
Since Multimodal Emotion Recognition in Conversation (MERC) can be applied to public
opinion monitoring, intelligent dialogue robots, and other fields, it has received extensive …
opinion monitoring, intelligent dialogue robots, and other fields, it has received extensive …
Revisiting multimodal emotion recognition in conversation from the perspective of graph spectrum
Efficiently capturing consistent and complementary semantic features in a multimodal
conversation context is crucial for Multimodal Emotion Recognition in Conversation (MERC) …
conversation context is crucial for Multimodal Emotion Recognition in Conversation (MERC) …
Revisiting multi-modal emotion learning with broad state space models and probability-guidance fusion
Multi-modal Emotion Recognition in Conversation (MERC) has received considerable
attention in various fields, eg, human-computer interaction and recommendation systems …
attention in various fields, eg, human-computer interaction and recommendation systems …
Contrastive graph representation learning with adversarial cross-view reconstruction and information bottleneck
Abstract Graph Neural Networks (GNNs) have received extensive research attention due to
their powerful information aggregation capabilities. Despite the success of GNNs, most of …
their powerful information aggregation capabilities. Despite the success of GNNs, most of …
Der-gcn: Dialogue and event relation-aware graph convolutional neural network for multimodal dialogue emotion recognition
With the continuous development of deep learning (DL), the task of multimodal dialogue
emotion recognition (MDER) has recently received extensive research attention, which is …
emotion recognition (MDER) has recently received extensive research attention, which is …
Adversarial representation with intra-modal and inter-modal graph contrastive learning for multimodal emotion recognition
With the release of increasing open-source emotion recognition datasets on social media
platforms and the rapid development of computing resources, multimodal emotion …
platforms and the rapid development of computing resources, multimodal emotion …
Contrastive multi-graph learning with neighbor hierarchical sifting for semi-supervised text classification
Graph contrastive learning has been successfully applied in text classification due to its
remarkable ability for self-supervised node representation learning. However, explicit graph …
remarkable ability for self-supervised node representation learning. However, explicit graph …
Efficient long-distance latent relation-aware graph neural network for multi-modal emotion recognition in conversations
The task of multi-modal emotion recognition in conversation (MERC) aims to analyze the
genuine emotional state of each utterance based on the multi-modal information in the …
genuine emotional state of each utterance based on the multi-modal information in the …
Spegcl: Self-supervised graph spectrum contrastive learning without positive samples
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 …
making it popular in various fields (eg, social networks, and knowledge graphs). Our study …