Summary of Harnessing Shared Relations Via Multimodal Mixup Contrastive Learning For Multimodal Classification, by Raja Kumar et al.
Harnessing Shared Relations via Multimodal Mixup Contrastive Learning for Multimodal Classification
by Raja Kumar, Raghav Singhal, Pranamya Kulkarni, Deval Mehta, Kshitij Jadhav
First submitted to arxiv on: 26 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 paper proposes a new approach called M3CoL for multimodal learning, which captures nuanced shared relations between different modalities like text, images, and audio. The authors use a contrastive loss function that aligns mixed samples from one modality with corresponding samples from other modalities. They also introduce a fusion module to integrate predictions from unimodal models during training. The approach is evaluated on various datasets, including N24News, ROSMAP, BRCA, and Food-101, and outperforms state-of-the-art methods on some datasets while achieving comparable performance on others. The paper highlights the importance of learning shared relations for robust multimodal learning and opens up avenues for future research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about a new way to learn from multiple types of data, like text, pictures, and audio. It helps computers understand how these different forms of data relate to each other. The approach uses a special kind of learning called contrastive learning that helps the computer focus on similarities between different types of data. This can help with tasks like classifying news articles or recognizing food products in images. The paper shows that this new approach works well on several datasets and could be useful for many applications. |
Keywords
» Artificial intelligence » Contrastive loss
