Summary of Multimodal Classification Via Modal-aware Interactive Enhancement, by Qing-yuan Jiang et al.
Multimodal Classification via Modal-Aware Interactive Enhancement
by Qing-Yuan Jiang, Zhouyang Chi, Yang Yang
First submitted to arxiv on: 5 Jul 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 The proposed modal-aware interactive enhancement (MIE) method aims to address the optimization imbalance issue in multimodal learning by utilizing sharpness aware minimization (SAM). The MIE method combines an optimization strategy based on SAM during the forward phase with a gradient modification strategy during the backward phase, which enables better interaction between model information and alleviates modality forgetting. Experimental results on various datasets demonstrate that MIE outperforms state-of-the-art baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a big problem in learning from multiple sources of data. Right now, it’s hard to make computers learn equally well from different types of data, like images and words. The new method, called modal-aware interactive enhancement (MIE), makes this process work better by smoothing out the way the computer learns and helping different types of data work together more smoothly. This means that computers can understand and use information from multiple sources more effectively. |
Keywords
» Artificial intelligence » Optimization » Sam
