Summary of Attribution Regularization For Multimodal Paradigms, by Sahiti Yerramilli et al.
Attribution Regularization for Multimodal Paradigms
by Sahiti Yerramilli, Jayant Sravan Tamarapalli, Jonathan Francis, Eric Nyberg
First submitted to arxiv on: 2 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 regularization term to enhance the performance of multimodal machine learning models that integrate information from multiple modalities. The current state-of-the-art multimodal models often underperform their unimodal counterparts despite having access to richer information. This is due to the dominant influence of a single modality, leading to suboptimal decision-making processes. To address this challenge, the proposed regularization term encourages multimodal models to effectively utilize information from all modalities when making decisions. The focus of this project lies in the video-audio domain, but the proposed technique holds promise for broader applications in embodied AI research where multiple modalities are involved. The paper aims to mitigate the issue of unimodal dominance and improve the performance of multimodal machine learning systems through extensive experimentation and evaluation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research proposes a new way to make machines that learn from multiple sources, like videos and audio, work better together. Right now, these machines often don’t use all the information they have access to because one type of data dominates the others. This project aims to fix this problem by adding a special filter to the machine learning process. The focus is on using this technique with video and audio data, but it could be used in other areas where different types of data are involved. |
Keywords
* Artificial intelligence * Machine learning * Regularization
