Summary of Reconboost: Boosting Can Achieve Modality Reconcilement, by Cong Hua et al.
ReconBoost: Boosting Can Achieve Modality Reconcilement
by Cong Hua, Qianqian Xu, Shilong Bao, Zhiyong Yang, Qingming Huang
First submitted to arxiv on: 15 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 In this paper, researchers propose a novel multi-modal learning paradigm that reconciles the exploitation of uni-modal features and exploration of cross-modal interactions. Current approaches often explore multiple modalities simultaneously, which can lead to modality competition, where one modality dominates the learning process. To address this issue, the authors introduce ReconBoost, a method that updates a fixed modality each time, adjusting the learning objective with a reconciliation regularization against historical models. The approach resembles Friedman’s Gradient-Boosting algorithm, but only preserves the newest model for each modality to avoid overfitting. Experiments on six multi-modal benchmarks demonstrate the effectiveness of ReconBoost. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding a new way to learn from different types of data at the same time. Right now, learning from multiple types of data can be like trying to talk to someone who speaks many languages – sometimes one language gets more attention than others. The researchers want to fix this by creating an algorithm that takes turns exploring each type of data, making sure none gets left behind. They call this algorithm ReconBoost and show it works well on several different types of data. |
Keywords
» Artificial intelligence » Attention » Boosting » Multi modal » Overfitting » Regularization
