Papers with MOSEI datasets
COGMEN: COntextualized GNN based Multimodal Emotion recognitioN (2022.naacl-main)
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| Challenge: | During a conversation, a person’s emotions are influenced by the other speaker’s utterances and their own emotional state over the utterrances. |
| Approach: | They propose a Graph Neural Network based Multi-modal Emotion recognitioN system that leverages local and global information in a conversation. |
| Outcome: | The proposed system gives state-of-the-art results on IEMOCAP and MOSEI datasets and detailed ablation experiments show the importance of modeling information at both levels. |
Contextual Inter-modal Attention for Multi-modal Sentiment Analysis (D18-1)
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Deepanway Ghosal, Md Shad Akhtar, Dushyant Chauhan, Soujanya Poria, Asif Ekbal, Pushpak Bhattacharyya
| Challenge: | Existing methods for multi-modal sentiment analysis are limited due to the use of text, visual and acoustic inputs. |
| Approach: | They propose a recurrent neural network based multi-modal attention framework that leverages contextual information for utterance-level sentiment prediction. |
| Outcome: | The proposed framework performs better on two multi-modal sentiment analysis benchmark datasets with accuracies of 82.31% and 79.80% for the MOSI and MOSEI datasets. |
QAP: A Quantum-Inspired Adaptive-Priority-Learning Model for Multimodal Emotion Recognition (2023.findings-acl)
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| Challenge: | Experimental results show that multimodal emotion recognition is a state-of-the-art technique . textual, visual and acoustic modalities are involved in multimodal video emotion recognition . |
| Approach: | They propose a quantum-inspired adaptive-priority-learning model to address the challenges . they use quantum state to model modal features and Q-attention to integrate three modalities . |
| Outcome: | Experimental results show that QAP improves on previous models. |