Deep Multimodal Learning with Missing Modality: A Survey
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866915771851997184 |
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| author | Wu, Renjie Wang, Hu Chen, Hsiang-Ting Carneiro, Gustavo |
| author_facet | Wu, Renjie Wang, Hu Chen, Hsiang-Ting Carneiro, Gustavo |
| contents | During multimodal model training and testing, certain data modalities may be absent due to sensor limitations, cost constraints, privacy concerns, or data loss, negatively affecting performance. Multimodal learning techniques designed to handle missing modalities can mitigate this by ensuring model robustness even when some modalities are unavailable. This survey reviews recent progress in Multimodal Learning with Missing Modality (MLMM), focusing on deep learning methods. It provides the first comprehensive survey that covers the motivation and distinctions between MLMM and standard multimodal learning setups, followed by a detailed analysis of current methods, applications, and datasets, concluding with challenges and future directions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_07825 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Deep Multimodal Learning with Missing Modality: A Survey Wu, Renjie Wang, Hu Chen, Hsiang-Ting Carneiro, Gustavo Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning During multimodal model training and testing, certain data modalities may be absent due to sensor limitations, cost constraints, privacy concerns, or data loss, negatively affecting performance. Multimodal learning techniques designed to handle missing modalities can mitigate this by ensuring model robustness even when some modalities are unavailable. This survey reviews recent progress in Multimodal Learning with Missing Modality (MLMM), focusing on deep learning methods. It provides the first comprehensive survey that covers the motivation and distinctions between MLMM and standard multimodal learning setups, followed by a detailed analysis of current methods, applications, and datasets, concluding with challenges and future directions. |
| title | Deep Multimodal Learning with Missing Modality: A Survey |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2409.07825 |