Summary of Robust Multimodal Learning Via Representation Decoupling, by Shicai Wei et al.
Robust Multimodal Learning via Representation Decoupling
by Shicai Wei, Yang Luo, Yuji Wang, Chunbo Luo
First submitted to arxiv on: 5 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 proposes a novel approach to multimodal learning robust to missing modalities. Existing methods learn a common subspace representation for different modality combinations, but these approaches are sub-optimal due to implicit constraints on intra-class representations. The proposed Decoupled Multimodal Representation Network (DMRNet) models input from different modality combinations as probabilistic distributions, allowing the model to capture modality-specific information and relax direction constraints. This approach is demonstrated to significantly outperform state-of-the-art methods on multimodal classification and segmentation tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way of learning with multiple types of data, like images and sound. Current approaches try to combine these different types of data into a single representation, but this can limit what the model can learn. The proposed method, DMRNet, treats each type of data as a separate probability distribution, allowing it to capture unique information from each one. This approach is shown to work well on various tasks involving multiple types of data. |
Keywords
» Artificial intelligence » Classification » Probability
