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Found 8 papers, 0 papers with code
A Two-Stage Multimodal Emotion Recognition Model Based on Graph Contrastive Learning
To address the above issues, we propose a two-stage emotion recognition model based on graph contrastive learning (TS-GCL).
Adversarial Representation with Intra-Modal and Inter-Modal Graph Contrastive Learning for Multimodal Emotion Recognition
However, the existing feature fusion methods have usually mapped the features of different modalities into the same feature space for information fusion, which can not eliminate the heterogeneity between different modalities.
DER-GCN: Dialogue and Event Relation-Aware Graph Convolutional Neural Network for Multimodal Dialogue Emotion Recognition
Specifically, we construct a weighted multi-relationship graph to simultaneously capture the dependencies between speakers and event relations in a dialogue.
Deep Imbalanced Learning for Multimodal Emotion Recognition in Conversations
The main task of Multimodal Emotion Recognition in Conversations (MERC) is to identify the emotions in modalities, e. g., text, audio, image and video, which is a significant development direction for realizing machine intelligence.
A Comprehensive Survey on Multi-modal Conversational Emotion Recognition with Deep Learning
Unlike the traditional single-utterance multi-modal emotion recognition or single-modal conversation emotion recognition, MCER is a more challenging problem that needs to deal with more complex emotional interaction relationships.
Graph Information Bottleneck for Remote Sensing Segmentation
Furthermore, this paper innovatively introduces information bottleneck theory into graph contrastive learning to maximize task-related information while minimizing task-independent redundant information.
CZL-CIAE: CLIP-driven Zero-shot Learning for Correcting Inverse Age Estimation
Zero-shot age estimation aims to learn feature information about age from input images and make inferences about a given person's image or video frame without specific sample data.
A Low-rank Matching Attention based Cross-modal Feature Fusion Method for Conversational Emotion Recognition
By setting a matching weight and calculating attention scores between modal features row by row, LMAM contains fewer parameters than the self-attention method.
