Papers with unimodal representations
Improving Multimodal fusion via Mutual Dependency Maximisation (2021.emnlp-main)
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| 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. |
UniS-MMC: Multimodal Classification via Unimodality-supervised Multimodal Contrastive Learning (2023.findings-acl)
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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. |
| 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. |
Efficient Low-rank Multimodal Fusion With Modality-Specific Factors (P18-1)
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Zhun Liu, Ying Shen, Varun Bharadhwaj Lakshminarasimhan, Paul Pu Liang, AmirAli Bagher Zadeh, Louis-Philippe Morency
| Challenge: | Multimodal research is a growing field of artificial intelligence, and fusion is one of the main research problems. |
| Approach: | They propose a low-rank multimodal fusion method which integrates multiple unimodal representations into one compact multimodal representation. |
| Outcome: | The proposed method achieves competitive results on multimodal sentiment analysis, speaker trait analysis, and emotion recognition tasks while reducing computational complexity. |
The Effects of Unimodal Representation Choices on Multimodal Learning (L18-1)
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| Challenge: | In the real world, multiple modes of information are gathered to create knowledge in a way humans can understand. |
| Approach: | They propose to combine unimodal representations to map multiple modes of information to a single space . they argue that the way they are combined can affect performance and classification metrics . |
| Outcome: | The proposed model can be used to correlate words in a textual description of an object with multimodal representations. |
CH-SIMS: A Chinese Multimodal Sentiment Analysis Dataset with Fine-grained Annotation of Modality (2020.acl-main)
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| 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. |
Unimodal and Crossmodal Refinement Network for Multimodal Sequence Fusion (2021.emnlp-main)
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| Challenge: | Existing approaches to modulate one modal feature to another are lacking in multimodal representation learning. |
| Approach: | They propose to use unimodal and crossmodal refinement networks to enhance uni and cross-modal representations by iterative updating of distributions with transformer-based attention layers to refine modality-specific learning. |
| Outcome: | The proposed network outperforms state-of-the-art techniques on MOSI and MOSEI datasets. |
Improving Multimodal Sentiment Analysis: Supervised Angular margin-based Contrastive Learning for Enhanced Fusion Representation (2023.findings-emnlp)
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| 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. |