SDR-GNN: Spectral Domain Reconstruction Graph Neural Network for Incomplete Multimodal Learning in Conversational Emotion Recognition

• URL: http://arxiv.org/abs/2411.19822v1
• Date: Fri, 29 Nov 2024 16:31:50 GMT
• Title: SDR-GNN: Spectral Domain Reconstruction Graph Neural Network for Incomplete Multimodal Learning in Conversational Emotion Recognition
• Authors: Fangze Fu, Wei Ai, Fan Yang, Yuntao Shou, Tao Meng, Keqin Li,
• Abstract summary: Multimodal Emotion Recognition in Conversations (MERC) aims to classify utterance emotions using textual, auditory, and visual modal features. Most existing MERC methods assume each utterance has complete modalities, overlooking the common issue of incomplete modalities in real-world scenarios. We propose a Spectral Domain Reconstruction Graph Neural Network (SDR-GNN) for incomplete multimodal learning in conversational emotion recognition.
• Score: 14.645598552036908
• License:
• Abstract: Multimodal Emotion Recognition in Conversations (MERC) aims to classify utterance emotions using textual, auditory, and visual modal features. Most existing MERC methods assume each utterance has complete modalities, overlooking the common issue of incomplete modalities in real-world scenarios. Recently, graph neural networks (GNNs) have achieved notable results in Incomplete Multimodal Emotion Recognition in Conversations (IMERC). However, traditional GNNs focus on binary relationships between nodes, limiting their ability to capture more complex, higher-order information. Moreover, repeated message passing can cause over-smoothing, reducing their capacity to preserve essential high-frequency details. To address these issues, we propose a Spectral Domain Reconstruction Graph Neural Network (SDR-GNN) for incomplete multimodal learning in conversational emotion recognition. SDR-GNN constructs an utterance semantic interaction graph using a sliding window based on both speaker and context relationships to model emotional dependencies. To capture higher-order and high-frequency information, SDR-GNN utilizes weighted relationship aggregation, ensuring consistent semantic feature extraction across utterances. Additionally, it performs multi-frequency aggregation in the spectral domain, enabling efficient recovery of incomplete modalities by extracting both high- and low-frequency information. Finally, multi-head attention is applied to fuse and optimize features for emotion recognition. Extensive experiments on various real-world datasets demonstrate that our approach is effective in incomplete multimodal learning and outperforms current state-of-the-art methods.

Related papers

• Effective Context Modeling Framework for Emotion Recognition in Conversations [2.7175580940471913]
  Emotion Recognition in Conversations (ERC) facilitates a deeper understanding of the emotions conveyed by speakers in each utterance within a conversation. Recent Graph Neural Networks (GNNs) have demonstrated their strengths in capturing data relationships. We propose ConxGNN, a novel GNN-based framework designed to capture contextual information in conversations.
  arXiv  Detail & Related papers  (2024-12-21T02:22:06Z)
• Efficient Long-distance Latent Relation-aware Graph Neural Network for Multi-modal Emotion Recognition in Conversations [8.107561045241445]
  We propose an Efficient Long-distance Latent Relation-aware Graph Neural Network (ELR-GNN) for multi-modal emotion recognition in conversations. ELR-GNN achieves state-of-the-art performance on the benchmark IEMOCAP and MELD, with running times reduced by 52% and 35%, respectively.
  arXiv  Detail & Related papers  (2024-06-27T15:54:12Z)
• Revisiting Multimodal Emotion Recognition in Conversation from the Perspective of Graph Spectrum [13.81570624162769]
  We propose a Graph-Spectrum-based Multimodal Consistency and Complementary collaborative learning framework GS-MCC. First, GS-MCC uses a sliding window to construct a multimodal interaction graph to model conversational relationships. Then, GS-MCC uses contrastive learning to construct self-supervised signals that reflect complementarity and consistent semantic collaboration.
  arXiv  Detail & Related papers  (2024-04-27T10:47:07Z)
• Conversation Understanding using Relational Temporal Graph Neural Networks with Auxiliary Cross-Modality Interaction [2.1261712640167856]
  Emotion recognition is a crucial task for human conversation understanding. We propose an input Temporal Graph Neural Network with Cross-Modality Interaction (CORECT) CORECT effectively captures conversation-level cross-modality interactions and utterance-level temporal dependencies.
  arXiv  Detail & Related papers  (2023-11-08T07:46:25Z)
• EMERSK -- Explainable Multimodal Emotion Recognition with Situational Knowledge [0.0]
  We present Explainable Multimodal Emotion Recognition with Situational Knowledge (EMERSK) EMERSK is a general system for human emotion recognition and explanation using visual information. Our system can handle multiple modalities, including facial expressions, posture, and gait in a flexible and modular manner.
  arXiv  Detail & Related papers  (2023-06-14T17:52:37Z)
• Surrogate Gradient Spiking Neural Networks as Encoders for Large Vocabulary Continuous Speech Recognition [91.39701446828144]
  We show that spiking neural networks can be trained like standard recurrent neural networks using the surrogate gradient method. They have shown promising results on speech command recognition tasks. In contrast to their recurrent non-spiking counterparts, they show robustness to exploding gradient problems without the need to use gates.
  arXiv  Detail & Related papers  (2022-12-01T12:36:26Z)
• Multimodal Emotion Recognition using Transfer Learning from Speaker Recognition and BERT-based models [53.31917090073727]
  We propose a neural network-based emotion recognition framework that uses a late fusion of transfer-learned and fine-tuned models from speech and text modalities. We evaluate the effectiveness of our proposed multimodal approach on the interactive emotional dyadic motion capture dataset.
  arXiv  Detail & Related papers  (2022-02-16T00:23:42Z)
• Improved Speech Emotion Recognition using Transfer Learning and Spectrogram Augmentation [56.264157127549446]
  Speech emotion recognition (SER) is a challenging task that plays a crucial role in natural human-computer interaction. One of the main challenges in SER is data scarcity. We propose a transfer learning strategy combined with spectrogram augmentation.
  arXiv  Detail & Related papers  (2021-08-05T10:39:39Z)
• Continuous Emotion Recognition with Spatiotemporal Convolutional Neural Networks [82.54695985117783]
  We investigate the suitability of state-of-the-art deep learning architectures for continuous emotion recognition using long video sequences captured in-the-wild. We have developed and evaluated convolutional recurrent neural networks combining 2D-CNNs and long short term-memory units, and inflated 3D-CNN models, which are built by inflating the weights of a pre-trained 2D-CNN model during fine-tuning.
  arXiv  Detail & Related papers  (2020-11-18T13:42:05Z)
• Learning Deep Interleaved Networks with Asymmetric Co-Attention for Image Restoration [65.11022516031463]
  We present a deep interleaved network (DIN) that learns how information at different states should be combined for high-quality (HQ) images reconstruction. In this paper, we propose asymmetric co-attention (AsyCA) which is attached at each interleaved node to model the feature dependencies. Our presented DIN can be trained end-to-end and applied to various image restoration tasks.
  arXiv  Detail & Related papers  (2020-10-29T15:32:00Z)
• MS-Net: Multi-Site Network for Improving Prostate Segmentation with Heterogeneous MRI Data [75.73881040581767]
  We propose a novel multi-site network (MS-Net) for improving prostate segmentation by learning robust representations. Our MS-Net improves the performance across all datasets consistently, and outperforms state-of-the-art methods for multi-site learning.
  arXiv  Detail & Related papers  (2020-02-09T14:11:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.