Papers with Multimodal affective computing
Multimodal Affective Analysis Using Hierarchical Attention Strategy with Word-Level Alignment (P18-1)
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| Challenge: | Existing approaches to classify human affect and subjective information from multiple data sources are limited by the lack of high-level feature associations. |
| Approach: | They propose a hierarchical multimodal architecture with attention and word-level fusion to classify utterance-level sentiment and emotion from text and audio data. |
| Outcome: | The proposed model outperforms state-of-the-art approaches on published datasets and visualizes and interprets synchronized attention over modalities. |
Learning Invariant Modality Representation for Robust Multimodal Learning from a Causal Inference Perspective (2026.acl-long)
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| Challenge: | Existing approaches to multimodal affective computing learn spurious correlations from training data rather than genuine causal relationships, harming generalization under distribution shifts or noisy modalities. |
| Approach: | They propose a causal modality-invariant representation framework that separates each modality into ‘causal invariant’ and ‘environment-specific spurious representation’ from a modal inference perspective. |
| Outcome: | Experiments on multiple multimodal benchmarks show that the proposed framework achieves state-of-the-art performance. |