Summary of Classifier-guided Gradient Modulation For Enhanced Multimodal Learning, by Zirun Guo et al.
Classifier-guided Gradient Modulation for Enhanced Multimodal Learning
by Zirun Guo, Tao Jin, Jingyuan Chen, Zhou Zhao
First submitted to arxiv on: 3 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); 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 Classifier-Guided Gradient Modulation (CGGM) method balances multimodal learning by considering both the magnitude and directions of gradients. This addresses limitations in existing methods that only modulate gradient magnitudes. The CGGM outperforms baselines and state-of-the-art methods on four multimodal datasets, including UPMC-Food 101, CMU-MOSI, IEMOCAP, and BraTS 2021, for tasks such as classification, regression, and segmentation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers has developed a new way to help machines learn from multiple types of data at the same time. Right now, when training machines on different kinds of information, they tend to focus on one type and ignore the others. To fix this, they created a new method called Classifier-Guided Gradient Modulation (CGGM). This approach makes sure that the machine is using all the information it’s given, not just some of it. The researchers tested their method on four different datasets and found that it worked better than other methods for tasks like identifying objects in pictures or understanding what people are saying. |
Keywords
» Artificial intelligence » Classification » Regression
