Dynamic Fusion-Aware Graph Convolutional Neural Network for Multimodal Emotion Recognition in Conversations
arXiv:2603.22345v1 Announce Type: new Abstract: Multimodal emotion recognition in conversations (MERC) aims to identify and understand the emotions expressed by speakers during utterance interaction from multiple modalities (e.g., text, audio, images, etc.). Existing studies have shown that GCN can improve the performance of MERC by modeling dependencies between speakers. However, existing methods usually use fixed parameters to process multimodal features for different emotion types, ignoring the dynamics of fusion between different modalities, which forces the model to balance performance between multiple emotion categories, thus limiting the model's performance on some specific emotions. To this end, we propose a dynamic fusion-aware graph convolutional neural network (DF-GCN) for robust recognition of multimodal emotion features in conversations. Specifically, DF-GCN integrates ordinary differential equations into graph convolutional networks (GCNs) to {capture} the dynamic nature
arXiv:2603.22345v1 Announce Type: new Abstract: Multimodal emotion recognition in conversations (MERC) aims to identify and understand the emotions expressed by speakers during utterance interaction from multiple modalities (e.g., text, audio, images, etc.). Existing studies have shown that GCN can improve the performance of MERC by modeling dependencies between speakers. However, existing methods usually use fixed parameters to process multimodal features for different emotion types, ignoring the dynamics of fusion between different modalities, which forces the model to balance performance between multiple emotion categories, thus limiting the model's performance on some specific emotions. To this end, we propose a dynamic fusion-aware graph convolutional neural network (DF-GCN) for robust recognition of multimodal emotion features in conversations. Specifically, DF-GCN integrates ordinary differential equations into graph convolutional networks (GCNs) to {capture} the dynamic nature of emotional dependencies within utterance interaction networks and leverages the prompts generated by the global information vector (GIV) of the utterance to guide the dynamic fusion of multimodal features. This allows our model to dynamically change parameters when processing each utterance feature, so that different network parameters can be equipped for different emotion categories in the inference stage, thereby achieving more flexible emotion classification and enhancing the generalization ability of the model. Comprehensive experiments conducted on two public multimodal conversational datasets {confirm} that the proposed DF-GCN model delivers superior performance, benefiting significantly from the dynamic fusion mechanism introduced.
Executive Summary
The article introduces a novel Dynamic Fusion-Aware Graph Convolutional Neural Network (DF-GCN) to enhance multimodal emotion recognition in conversations. Traditional GCN-based approaches use fixed parameters for multimodal features across emotion types, limiting adaptability and performance specificity. DF-GCN addresses this by integrating ordinary differential equations into GCNs to capture dynamic emotional dependencies and leverages a global information vector (GIV) to guide dynamic fusion of multimodal inputs. This dynamic parameter adjustment allows the model to tailor processing to specific emotion categories during inference, improving classification flexibility and generalization. Experimental validation on public datasets confirms superior performance relative to existing methods. The work represents a meaningful advancement in dynamic modeling for multimodal affective analysis.
Key Points
- ▸ DF-GCN integrates ODEs into GCNs to capture dynamic emotional dependencies
- ▸ Global Information Vector (GIV) guides dynamic fusion of multimodal features
- ▸ Dynamic parameter adjustment enables tailored processing per emotion category during inference
Merits
Strength
DF-GCN’s dynamic fusion mechanism significantly enhances adaptability and performance specificity across emotion types, addressing a critical limitation in static GCN models
Demerits
Limitation
While promising, the complexity of integrating ODEs into GCNs may pose implementation challenges for practitioners unfamiliar with differential equation-based modeling, potentially affecting scalability or real-time applicability
Expert Commentary
This paper represents a sophisticated and timely contribution to multimodal emotion recognition. The authors elegantly bridge a well-documented gap in fixed-parameter GCN approaches by introducing a mathematically grounded dynamic fusion mechanism grounded in differential equations. The conceptual innovation lies not merely in adding complexity, but in aligning model architecture with the inherent variability of affective expression across modalities. The use of GIV as a prompt-based guide for dynamic fusion is particularly noteworthy—it transforms the model from a static pattern recognizer into a context-sensitive interpreter. While the technical implementation may introduce hurdles for rapid deployment, the theoretical and empirical gains are substantial. This work sets a new benchmark for adaptive modeling in affective AI and invites further exploration of differential equation-based architectures in other domains of human-computer interaction.
Recommendations
- ✓ 1. Developers should consider integrating DF-GCN into existing multimodal AI pipelines where emotion specificity is critical.
- ✓ 2. Future research should extend DF-GCN to longitudinal conversational data to validate its effectiveness in evolving affective contexts over time.
Sources
Original: arXiv - cs.AI
