# GA2MIF: Graph and Attention Based Two-Stage Multi-Source Information Fusion for Conversational Emotion Detection

arXiv: https://arxiv.org/abs/2207.11900 · HTML: https://mlsanity.com/paper/2207.11900/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jul 2022.

## Built on by (most cited first, 20 of 20)

- [Revisiting Multimodal Emotion Recognition in Conversation from the Perspective of Graph Spectrum](https://mlsanity.com/paper/2404.17862/) (2024)
- [Efficient Long-distance Latent Relation-aware Graph Neural Network for Multi-modal Emotion Recognition in Conversations](https://mlsanity.com/paper/2407.00119/) (2024)
- [Deep Emotion Recognition in Textual Conversations: A Survey](https://mlsanity.com/paper/2211.09172/) (2022)
- [Recent Advances in Multimodal Affective Computing: An NLP Perspective](https://mlsanity.com/paper/2409.07388/) (2024)
- [TelME: Teacher-leading Multimodal Fusion Network for Emotion Recognition in Conversation](https://mlsanity.com/paper/2401.12987/) (2024)
- [MSE-Adapter: A Lightweight Plugin Endowing LLMs with the Capability to Perform Multimodal Sentiment Analysis and Emotion Recognition](https://mlsanity.com/paper/2502.12478/) (2025)
- [UniMEEC: Towards Unified Multimodal Emotion Recognition and Emotion Cause](https://mlsanity.com/paper/2404.00403/) (2024)
- [EmotionIC: emotional inertia and contagion-driven dependency modeling for emotion recognition in conversation](https://mlsanity.com/paper/2303.11117/) (2023)
- [Navigating the Emotion Tree: Hierarchical Hyperbolic RAG for Multimodal Emotion Recognition](https://mlsanity.com/paper/2605.18884/) (2026)
- [Mamba-Enhanced Text-Audio-Video Alignment Network for Emotion Recognition in Conversations](https://mlsanity.com/paper/2409.05243/) (2024)
- [EmoVerse: Exploring Multimodal Large Language Models for Sentiment and Emotion Understanding](https://mlsanity.com/paper/2412.08049/) (2024)
- [VISTANet: VIsual Spoken Textual Additive Net for Interpretable Multimodal Emotion Recognition](https://mlsanity.com/paper/2208.11450/) (2022)
- [Towards Data-centric Machine Learning on Directed Graphs: a Survey](https://mlsanity.com/paper/2412.01849/) (2024)
- [PEER: Unified Process-Outcome Reinforcement Learning for Structured Empathetic Reasoning](https://mlsanity.com/paper/2508.09521/) (2025)
- [EEG-Based Multimodal Learning via Hyperbolic Mixture-of-Curvature Experts](https://mlsanity.com/paper/2604.12579/) (2026)
- [Large, Complex, and Realistic Safety Clothing and Helmet Detection: Dataset and Method](https://mlsanity.com/paper/2306.02098/) (2023)
- [Expert-Guided Multimodal Fusion for Unified Emotion and Sentiment Analysis](https://mlsanity.com/paper/2601.07565/) (2026)
- [Disentangled Dual-Branch Graph Learning for Conversational Emotion Recognition](https://mlsanity.com/paper/2604.14204/) (2026)
- [MAF: Multimodal Adaptive Few-shot Prompting for Sentiment Analysis with MLLMs](https://mlsanity.com/paper/2606.15694/) (2026)
- [MVFA: A Multi-View Text-Guided Multimodal Fusion LLM Adapter for Sentiment Analysis and Emotion Recognition](https://mlsanity.com/paper/2609.06188/) (2026)

## Abstract (arXiv)

Multimodal Emotion Recognition in Conversation (ERC) plays an influential role in the field of human-computer interaction and conversational robotics since it can motivate machines to provide empathetic services. Multimodal data modeling is an up-and-coming research area in recent years, which is inspired by human capability to integrate multiple senses. Several graph-based approaches claim to capture interactive information between modalities, but the heterogeneity of multimodal data makes these methods prohibit optimal solutions. In this work, we introduce a multimodal fusion approach named Graph and Attention based Two-stage Multi-source Information Fusion (GA2MIF) for emotion detection in conversation. Our proposed method circumvents the problem of taking heterogeneous graph as input to the model while eliminating complex redundant connections in the construction of graph. GA2MIF focuses on contextual modeling and cross-modal modeling through leveraging Multi-head Directed Graph ATtention networks (MDGATs) and Multi-head Pairwise Cross-modal ATtention networks (MPCATs), respectively. Extensive experiments on two public datasets (i.e., IEMOCAP and MELD) demonstrate that the proposed GA2MIF has the capacity to validly capture intra-modal long-range contextual information and inter-modal complementary information, as well as outperforms the prevalent State-Of-The-Art (SOTA) models by a remarkable margin.