Mixture of Disentangled Experts with Missing Modalities for Robust Multimodal Sentiment Analysis
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866915768577294336 |
|---|---|
| author | Li, Xiang Zhang, Xiaoming Miao, Dezhuang Cheng, Xianfu Li, Dawei Han, Honggui Li, Zhoujun |
| author_facet | Li, Xiang Zhang, Xiaoming Miao, Dezhuang Cheng, Xianfu Li, Dawei Han, Honggui Li, Zhoujun |
| contents | Multimodal Sentiment Analysis (MSA) integrates multiple modalities to infer human sentiment, but real-world noise often leads to missing or corrupted data. However, existing feature-disentangled methods struggle to handle the internal variations of heterogeneous information under uncertain missingness, making it difficult to learn effective multimodal representations from degraded modalities. To address this issue, we propose DERL, a Disentangled Expert Representation Learning framework for robust MSA. Specifically, DERL employs hybrid experts to adaptively disentangle multimodal inputs into orthogonal private and shared representation spaces. A multi-level reconstruction strategy is further developed to provide collaborative supervision, enhancing both the expressiveness and robustness of the learned representations. Finally, the disentangled features act as modality experts with distinct roles to generate importance-aware fusion results. Extensive experiments on two MSA benchmarks demonstrate that DERL outperforms state-of-the-art methods under various missing-modality conditions. For instance, our method achieves improvements of 2.47% in Acc-2 and 2.25% in MAE on MOSI under intra-modal missingness. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_01833 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Mixture of Disentangled Experts with Missing Modalities for Robust Multimodal Sentiment Analysis Li, Xiang Zhang, Xiaoming Miao, Dezhuang Cheng, Xianfu Li, Dawei Han, Honggui Li, Zhoujun Multimedia Multimodal Sentiment Analysis (MSA) integrates multiple modalities to infer human sentiment, but real-world noise often leads to missing or corrupted data. However, existing feature-disentangled methods struggle to handle the internal variations of heterogeneous information under uncertain missingness, making it difficult to learn effective multimodal representations from degraded modalities. To address this issue, we propose DERL, a Disentangled Expert Representation Learning framework for robust MSA. Specifically, DERL employs hybrid experts to adaptively disentangle multimodal inputs into orthogonal private and shared representation spaces. A multi-level reconstruction strategy is further developed to provide collaborative supervision, enhancing both the expressiveness and robustness of the learned representations. Finally, the disentangled features act as modality experts with distinct roles to generate importance-aware fusion results. Extensive experiments on two MSA benchmarks demonstrate that DERL outperforms state-of-the-art methods under various missing-modality conditions. For instance, our method achieves improvements of 2.47% in Acc-2 and 2.25% in MAE on MOSI under intra-modal missingness. |
| title | Mixture of Disentangled Experts with Missing Modalities for Robust Multimodal Sentiment Analysis |
| topic | Multimedia |
| url | https://arxiv.org/abs/2602.01833 |