arXiv:2411.10060v1 Announce Type: new
Abstract: Multimodal emotion recognition in conversation (MER) aims to accurately identify emotions in conversational utterances by integrating multimodal information. Previous methods usually treat multimodal information as equal quality and employ symmetric architectures to conduct multimodal fusion. However, in reality, the quality of different modalities usually varies considerably, and utilizing a symmetric architecture is difficult to accurately recognize conversational emotions when dealing with uneven modal information. Furthermore, fusing multi-modality information in a single granularity may fail to adequately integrate modal information, exacerbating the inaccuracy in emotion recognition. In this paper, we propose a novel Cross-Modality Augmented Transformer with Hierarchical Variational Distillation, called CMATH, which consists of two major components, i.e., Multimodal Interaction Fusion and Hierarchical Variational Distillation. The former is comprised of two submodules, including Modality Reconstruction and Cross-Modality Augmented Transformer (CMA-Transformer), where Modality Reconstruction focuses on obtaining high-quality compressed representation of each modality, and CMA-Transformer adopts an asymmetric fusion strategy which treats one modality as the central modality and takes others as auxiliary modalities. The latter first designs a variational fusion network to fuse the fine-grained representations learned by CMA- Transformer into a coarse-grained representations. Then, it introduces a hierarchical distillation framework to maintain the consistency between modality representations with different granularities. Experiments on the IEMOCAP and MELD datasets demonstrate that our proposed model outperforms previous state-of-the-art baselines. Implementation codes can be available at https://github.com/ cjw-MER/CMATH.
Analysis of the Content
In this article, the authors discuss the challenges and limitations of previous methods in multimodal emotion recognition in conversation (MER) and propose a novel approach called Cross-Modality Augmented Transformer with Hierarchical Variational Distillation (CMATH). The authors highlight the importance of considering the varying quality of different modalities and the need for an asymmetric fusion strategy to accurately recognize conversational emotions.
The concept of multimodal emotion recognition is highly relevant to the field of multimedia information systems. Multimodal information, which includes textual, visual, and auditory cues, is widely used in various multimedia applications such as video summarization, emotion detection in videos, and human-computer interaction. By accurately identifying emotions in conversational utterances, multimedia information systems can provide more personalized and interactive experiences.
CMATH addresses the limitations of previous methods by introducing two major components: Multimodal Interaction Fusion and Hierarchical Variational Distillation. The Modality Reconstruction submodule focuses on obtaining high-quality compressed representations of each modality, taking into account the varying quality of different modalities. The Cross-Modality Augmented Transformer (CMA-Transformer) submodule adopts an asymmetric fusion strategy, treating one modality as the central modality and others as auxiliary modalities. This approach allows for more accurate emotion recognition by leveraging the strengths of each modality.
The Hierarchical Variational Distillation component of CMATH further improves the fusion of multimodal information by designing a variational fusion network and a hierarchical distillation framework. The variational fusion network combines the fine-grained representations learned by the CMA-Transformer into a coarse-grained representation. This intermediate representation helps maintain consistency between different modalities with different granularities, ensuring a more accurate recognition of conversational emotions.
Expert Insights
The proposed CMATH model demonstrates the multi-disciplinary nature of the concepts discussed in the article. It combines techniques from natural language processing, computer vision, and machine learning to address the challenges in multimodal emotion recognition. This interdisciplinary approach is crucial for developing effective models that can accurately interpret and understand human emotions in conversational contexts.
Furthermore, the concept of CMATH aligns with the broader field of augmented reality, virtual reality, and artificial reality. Emotion recognition plays a crucial role in creating immersive and realistic virtual environments, where the system can respond appropriately to the user’s emotions and enhance the user’s overall experience. By accurately integrating multimodal information, such as facial expressions, speech intonations, and textual cues, CMATH can contribute to the advancement of emotion-aware virtual and augmented reality systems.
In conclusion, the authors’ proposed CMATH model addresses the challenges and limitations of previous methods in multimodal emotion recognition in conversation. The asymmetric fusion strategy and hierarchical variational distillation framework offer a robust solution for accurately recognizing conversational emotions. This research contributes to the wider field of multimedia information systems and has implications for augmented and virtual realities by enabling more immersive and emotionally responsive environments.