Self-Supervised Unimodal Label Generation Strategy Using Recalibrated Modality Representations for Multimodal Sentiment Analysis (2023.findings-eacl)
Copied to clipboard
| Challenge: | Multimodal sentiment analysis (MSA) has gained much attention over the last few years due to a lack of unimodal annotations in benchmark datasets. |
| Approach: | They propose a framework which integrates multimodal and unimodal tasks to optimize learning representations from multimodal data. |
| Outcome: | The proposed model learns to weight features differently based on features of other modalities and auto-generates unimodal annotations via a unimodule. |
Similar Papers
Which is Making the Contribution: Modulating Unimodal and Cross-modal Dynamics for Multimodal Sentiment Analysis (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Recent studies focus on learning cross-modal dynamics, but neglect to explore optimal solution for unimodal networks. |
| Approach: | They propose a new MSA framework to identify contribution of modalities and reduce impact of noisy information. |
| Outcome: | The proposed model outperforms state-of-the-art methods on publicly available datasets. |
UniMSE: Towards Unified Multimodal Sentiment Analysis and Emotion Recognition (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing studies study sentiment and emotion separately and do not fully exploit the complementary knowledge behind the two. |
| Approach: | They propose a multimodal sentiment knowledge-sharing framework that unifies MSA and ERC tasks from features, labels, and models. |
| Outcome: | The proposed framework achieves consistent improvements on four public benchmark datasets on MOSI, MOSEI, MELD, and IEMOCAP. |
Learning Language-guided Adaptive Hyper-modality Representation for Multimodal Sentiment Analysis (2023.emnlp-main)
Copied to clipboard
| 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. |
| Approach: | They propose an Adaptive Language-guided Multimodal Transformer (ALMT) that learns an irrelevance/conflict-suppressing representation from visual and audio features under guidance of language features at different scales. |
| Outcome: | The proposed model achieves state-of-the-art on several popular datasets and an abundance of ablation shows the effectiveness of the proposed model. |
Multimodal Contrastive Learning via Uni-Modal Coding and Cross-Modal Prediction for Multimodal Sentiment Analysis (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Recent work on multimodal representation learning has focused on uni-modality pre-training or cross-modalities integration. |
| Approach: | They propose a framework for multimodal representation learning that uses uni-modal contrastive coding and an efficient unimodal feature augmentation strategy to capture intermodal dynamics. |
| Outcome: | The proposed framework surpasses state-of-the-art methods on two public datasets. |
UniS-MMC: Multimodal Classification via Unimodality-supervised Multimodal Contrastive Learning (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing multimodal fusion methods ignore inter-modality relationship, treat each modality equally, suffer sensor noise, and thus reduce multimodal learning performance. |
| Approach: | They propose a multimodal contrastive method to explore more reliable multimodal representations under the weak supervision of unimodal predicting. |
| Outcome: | The proposed method outperforms current state-of-the-art multimodal learning methods on image-text classification benchmarks UPMC-Food-101 and N24News. |
Analyzing Modality Robustness in Multimodal Sentiment Analysis (2022.naacl-main)
Copied to clipboard
| Challenge: | despite its importance, little attention has been paid to improving the robustness of multimodal models. |
| Approach: | They propose simple diagnostic checks for modality robustness in a trained multimodal model . they find MSA models highly sensitive to a single modality, which creates issues . |
| Outcome: | The proposed checks show that models are highly sensitive to a single modality, which creates issues in their robustness. |
CH-SIMS: A Chinese Multimodal Sentiment Analysis Dataset with Fine-grained Annotation of Modality (2020.acl-main)
Copied to clipboard
| Challenge: | Existing studies in multimodal sentiment analysis only use unified multimodal annotations, which do not reflect the independent sentiment of single modalities. |
| Approach: | They propose a Chinese single- and multi-modal sentiment analysis dataset with multimodal and independent unimodal annotations that can be used to study the interaction between modalities. |
| Outcome: | The proposed methods achieve state-of-the-art performance and learn more distinctive unimodal representations. |
Improving Multimodal fusion via Mutual Dependency Maximisation (2021.emnlp-main)
Copied to clipboard
| Challenge: | Multimodal sentiment analysis is a trending area of research, and multimodal fusion is one of its most active topics. |
| Approach: | They propose to use modality-based penalties to measure dependency between models to improve accuracy. |
| Outcome: | The proposed methods improve accuracy on two well-known sentiment analysis datasets by 4.3 on the proposed models and by-product includes a statistical network which can interpret the high dimensional representations learnt by the model. |
Improving Multimodal Fusion with Hierarchical Mutual Information Maximization for Multimodal Sentiment Analysis (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing work on multimodal sentiment analysis relies on back-propagated task loss or geometric property of feature spaces to produce favorable fusion results. |
| Approach: | They propose a framework which hierarchically maximizes the Mutual Information (MI) in unimodal input pairs and between multimodal fusion result and unimod input to maintain task-related information through multimodal integration. |
| Outcome: | The proposed framework maximizes the Mutual Information (MI) in unimodal input pairs and between multimodal fusion result and unimodulated input to maintain task-related information through multimodal integration. |
CLGSI: A Multimodal Sentiment Analysis Framework based on Contrastive Learning Guided by Sentiment Intensity (2024.findings-naacl)
Copied to clipboard
| 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. |
| Approach: | They propose a framework for multimodal sentiment analysis based on contrastive learning guided by sentiment intensity (CLGSI) it selects positive and negative sample pairs based upon sentiment intensity differences and assigns corresponding weights accordingly. |
| Outcome: | The proposed framework extracts common features between different modalities and then uses them to predict sentiment intensity. |