Summary of Diagnosing and Re-learning For Balanced Multimodal Learning, by Yake Wei and Siwei Li and Ruoxuan Feng and Di Hu
Diagnosing and Re-learning for Balanced Multimodal Learning
by Yake Wei, Siwei Li, Ruoxuan Feng, Di Hu
First submitted to arxiv on: 12 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Multimedia (cs.MM)
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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 a novel approach to overcome the imbalanced multimodal learning problem, where models tend to focus on specific modalities over others. Existing methods control uni-modal encoders from different perspectives, but neglect the intrinsic limitation of modality capacity. The Diagnosing & Re-learning method estimates the learning state of each modality based on the separability of its uni-modal representation space and softly re-initializes the corresponding encoder. This approach avoids over-emphasizing scarcely informative modalities and enhances encoders of worse-learnt modalities, resulting in balanced and enhanced multimodal learning. The proposed method is evaluated on multiple types of modalities and multimodal frameworks, demonstrating superior performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper tries to solve a problem where AI models tend to focus on some ways of understanding information (like images or speech) over others. Right now, most methods try to control which parts of the model are learning from different types of data. But this paper says that’s not enough – it also needs to consider how much each type of data is “good” at being understood. To do this, they came up with a new way to adjust the model’s understanding of each type of data based on how well it can understand it. This helps the AI learn more evenly and make better decisions. |
Keywords
» Artificial intelligence » Encoder
