Challenge: Existing methods for multimodal sentiment analysis focus on forming positive and negative pairs, neglecting the variation in sentiment scores within the same class.
Approach: They propose a framework to enhance discrimination and generalizability of the multimodal representation and overcome biases in the fusion vector’s modality.
Outcome: The proposed model improves discrimination and generalizability of the multimodal representation and overcomes biases in the fusion vector’s modality.

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Challenge: Existing methods for multimodal sentiment detection do not consider token-level feature fusion.
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Multimodal Multi-loss Fusion Network for Sentiment Analysis (2024.naacl-long)

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Challenge: This paper examines the optimal selection and fusion of feature encoders across multiple modalities and combines them in one neural network to improve sentiment detection.
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CLGSI: A Multimodal Sentiment Analysis Framework based on Contrastive Learning Guided by Sentiment Intensity (2024.findings-naacl)

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Challenge: Recent studies have focused on contrastive learning, but lack detailed learning of the distribution of sample pairs with different sentiment intensity differences in the contrastive training representation space.
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Challenge: Multimodal Sentiment Analysis (MSA) is effective when using rich information from multiple sources, but the potential sentiment-irrelevant information across modalities may hinder the performance from being further improved.
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Challenge: Existing multimodal fusion methods ignore inter-modality relationship, treat each modality equally, suffer sensor noise, and thus reduce multimodal learning performance.
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Multimodal Contrastive Learning via Uni-Modal Coding and Cross-Modal Prediction for Multimodal Sentiment Analysis (2022.findings-emnlp)

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Challenge: Recent work on multimodal representation learning has focused on uni-modality pre-training or cross-modalities integration.
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Challenge: Multimodal Sentiment Analysis (MSA) aims to identify human attitudes from diverse modalities such as visual, audio and text.
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Sentiment Word Aware Multimodal Refinement for Multimodal Sentiment Analysis with ASR Errors (2022.findings-acl)

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Challenge: Existing models for multimodal sentiment analysis are limited in their capacity to be deployed in the real world.
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CTFN: Hierarchical Learning for Multimodal Sentiment Analysis Using Coupled-Translation Fusion Network (2021.acl-long)

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Challenge: Existing methods for multimodal sentiment analysis require all modalities as input, thus are sensitive to missing modality at predicting time.
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A Dual Contrastive Learning Framework for Enhanced Multimodal Conversational Emotion Recognition (2025.coling-main)

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Challenge: Existing methods struggle to capture emotion shifts due to label replication and fail to preserve positive independent modality contributions during fusion.
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