Summary of Mmbind: Unleashing the Potential Of Distributed and Heterogeneous Data For Multimodal Learning in Iot, by Xiaomin Ouyang et al.
MMBind: Unleashing the Potential of Distributed and Heterogeneous Data for Multimodal Learning in IoT
by Xiaomin Ouyang, Jason Wu, Tomoyoshi Kimura, Yihan Lin, Gunjan Verma, Tarek Abdelzaher, Mani Srivastava
First submitted to arxiv on: 18 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 Multimodal sensing systems are increasingly important in various real-world applications. Existing multimodal learning approaches rely heavily on training with a large amount of synchronized, complete multimodal data. However, this is impractical for real-world IoT sensing applications where data is typically collected by distributed nodes with heterogeneous data modalities and rarely labeled. To address this challenge, we propose MMBind, a new approach that constructs a pseudo-paired multimodal dataset for model training by binding data from disparate sources and incomplete modalities through a sufficiently descriptive shared modality. We also introduce a weighted contrastive learning approach to handle domain shifts among disparate data, coupled with an adaptive multimodal learning architecture capable of training models with heterogeneous modality combinations. Our evaluations on ten real-world multimodal datasets show that MMBind outperforms state-of-the-art baselines under varying degrees of data incompleteness and domain shift. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Multimodal sensing systems are used in many everyday applications, but they often have incomplete or mixed data. This makes it hard to train models that can understand this mixed data. The authors propose a new way to bind together different types of data to create a more complete dataset for training these models. They also introduce ways to handle changes in the type of data and how to adapt the model to learn from this data. Tests on real-world datasets show that their approach works better than other methods. |
