Papers with Supervised Angular-based Contrastive Learning

1 papers
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.

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