Summary of Mmp: Towards Robust Multi-modal Learning with Masked Modality Projection, by Niki Nezakati et al.
MMP: Towards Robust Multi-Modal Learning with Masked Modality Projection
by Niki Nezakati, Md Kaykobad Reza, Ameya Patil, Mashhour Solh, M. Salman Asif
First submitted to arxiv on: 3 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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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 This paper proposes Masked Modality Projection (MMP), a novel method for training multimodal learning models that can handle missing input modalities. By randomly masking a subset of modalities during training and learning to project available inputs, MMP enables the model to leverage information from available modalities to compensate for missing ones, enhancing robustness to different scenarios. The approach outperforms existing methods designed for specific modality combinations or missing modalities, demonstrating its effectiveness in various datasets and baseline models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps machines learn better by combining data from different sources. Sometimes, some of these sources might be missing, which can make the machine’s performance worse. The researchers came up with a new way to train machines that can handle this situation. They call it Masked Modality Projection (MMP). Instead of adapting to each combination of input sources separately, MMP trains one machine that can handle any scenario where some sources are missing. |
