Summary of Deep Multimodal Learning with Missing Modality: a Survey, by Renjie Wu et al.
Deep Multimodal Learning with Missing Modality: A Survey
by Renjie Wu, Hu Wang, Hsiang-Ting Chen, Gustavo Carneiro
First submitted to arxiv on: 12 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a comprehensive review of recent progress in Multimodal Learning with Missing Modality (MLMM), focusing on deep learning methods. The authors discuss the motivation behind MLMM, which aims to develop robust models that can handle missing modalities during training and testing. They provide an analysis of current MLMM methods, applications, and datasets, highlighting the importance of ensuring model performance even when certain data modalities are unavailable. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how machines learn from different types of data, like pictures or sounds. Sometimes, some of this data might be missing, which can make it harder for machines to understand what they’re learning. To solve this problem, scientists have developed special techniques that help machines learn even when some data is missing. This review looks at the latest research in this area and discusses how these techniques work, where they’re being used, and what challenges still need to be solved. |
Keywords
» Artificial intelligence » Deep learning
