Summary of Mmpareto: Boosting Multimodal Learning with Innocent Unimodal Assistance, by Yake Wei and Di Hu
MMPareto: Boosting Multimodal Learning with Innocent Unimodal Assistance
by Yake Wei, Di Hu
First submitted to arxiv on: 28 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); 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 The proposed MMPareto algorithm tackles the issue of gradient conflicts between multimodal and unimodal learning objectives, which can mislead optimization. The approach analyzes Pareto integration under a multimodal scenario and ensures a final gradient with direction common to all objectives and enhanced magnitude for improved generalization. The method is tested across multiple modalities and frameworks, demonstrating superior performance. This work has implications for multi-task cases with varying task difficulty, highlighting its scalability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Multimodal learning methods have shown great promise in solving imbalance problems, but a new challenge arises: the gradient conflict between multimodal and unimodal objectives. A team of researchers proposes an innovative solution to address this issue. They develop an algorithm that ensures a final gradient direction is common to all objectives and has improved magnitude for better generalization. The method is tested on different modalities and frameworks, showing great results. This breakthrough could lead to more effective multi-task learning and help with tasks that have varying difficulty levels. |
Keywords
* Artificial intelligence * Generalization * Multi task * Optimization
