Imbalanced multimodal learning that prioritizes the performance-dominant modality via unimodal ranking and asymmetric gradient modulation outperforms balanced approaches.
arXiv preprint arXiv:2106.11059 (2021)
3 Pith papers cite this work. Polarity classification is still indexing.
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BMLR reshapes the cross-modal label space to equalize mapping difficulty and balance optimization across modalities in multimodal learning.
A self-paced curriculum learning module with dual-level difficulty scoring improves weighted F1 scores by 1.2-10.4% when added to existing multimodal emotion recognition models on IEMOCAP and MELD.
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PDMP: Rethinking Balanced Multimodal Learning via Performance-Dominant Modality Prioritization
Imbalanced multimodal learning that prioritizes the performance-dominant modality via unimodal ranking and asymmetric gradient modulation outperforms balanced approaches.
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Balancing Multimodal Learning through Label Space Reshaping
BMLR reshapes the cross-modal label space to equalize mapping difficulty and balance optimization across modalities in multimodal learning.
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Leveraging Self-Paced Curriculum Learning for Enhanced Modality Balance in Multimodal Conversational Emotion Recognition
A self-paced curriculum learning module with dual-level difficulty scoring improves weighted F1 scores by 1.2-10.4% when added to existing multimodal emotion recognition models on IEMOCAP and MELD.