Papers by Shiqin Han
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. |
Supervised Attention Mechanism for Low-quality Multimodal Data (2025.emnlp-main)
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| Challenge: | Current studies address missing and noisy modalities separately in multimodal data . missing modality is often caused by unavailable data collection equipment or sensor failures . |
| Approach: | They propose a framework for multimodal affective computing that addresses missing and noisy modalities to enhance model robustness in low-quality data scenarios. |
| Outcome: | The proposed model outperforms state-of-the-art baselines on multiple datasets under the settings of complete modalities, missing modalités, and noisy modality. |