Deep learning-based multimodal emotion recognition from audio, visual, and text modalities: A systematic review of recent advancements and future prospects
S Zhang, Y Yang, C Chen, X Zhang, Q Leng… - Expert Systems with …, 2024 - Elsevier
Emotion recognition has recently attracted extensive interest due to its significant
applications to human–computer interaction. The expression of human emotion depends on …
applications to human–computer interaction. The expression of human emotion depends on …
Progress, achievements, and challenges in multimodal sentiment analysis using deep learning: A survey
A Pandey, DK Vishwakarma - Applied Soft Computing, 2024 - Elsevier
Sentiment analysis is a computational technique that analyses the subjective information
conveyed within a given expression. This encompasses appraisals, opinions, attitudes or …
conveyed within a given expression. This encompasses appraisals, opinions, attitudes or …
MM-DFN: Multimodal dynamic fusion network for emotion recognition in conversations
Emotion Recognition in Conversations (ERC) has considerable prospects for develo**
empathetic machines. For multimodal ERC, it is vital to understand context and fuse modality …
empathetic machines. For multimodal ERC, it is vital to understand context and fuse modality …
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, eg, text, audio, image, and video, which is a significant development …
emotions in modalities, eg, text, audio, image, and video, which is a significant development …
Multimodal emotion recognition using cross modal audio-video fusion with attention and deep metric learning
In the last few years, the multi-modal emotion recognition has become an important research
issue in the affective computing community due to its wide range of applications that include …
issue in the affective computing community due to its wide range of applications that include …
Dense graph convolutional with joint cross-attention network for multimodal emotion recognition
Multimodal emotion recognition (MER) has attracted much attention since it can leverage
consistency and complementary relationships across multiple modalities. However, previous …
consistency and complementary relationships across multiple modalities. However, previous …
A cross-modal fusion network based on self-attention and residual structure for multimodal emotion recognition
The audio-video based multimodal emotion recognition has attracted a lot of attention due to
its robust performance. Most of the existing methods focus on proposing different cross …
its robust performance. Most of the existing methods focus on proposing different cross …
Temporal sentiment localization: Listen and look in untrimmed videos
Video sentiment analysis aims to uncover the underlying attitudes of viewers, which has a
wide range of applications in real world. Existing works simply classify a video into a single …
wide range of applications in real world. Existing works simply classify a video into a single …
A multi-stage hierarchical relational graph neural network for multimodal sentiment analysis
P Gong, J Liu, X Zhang, X Li - ICASSP 2023-2023 IEEE …, 2023 - ieeexplore.ieee.org
Multimodal sentiment analysis targets at accurately perceiving the emotional states by
incorporating related information from multiple sources. However, existing methods mostly …
incorporating related information from multiple sources. However, existing methods mostly …
HiT-MST: Dynamic facial expression recognition with hierarchical transformers and multi-scale spatiotemporal aggregation
X **a, D Jiang - Information Sciences, 2023 - Elsevier
Facial expression recognition rarely explores complex spatiotemporal dependencies among
facial regions at different scales. This paper proposes a transformer-based three-layer …
facial regions at different scales. This paper proposes a transformer-based three-layer …