Summary of Explaining and Mitigating the Modality Gap in Contrastive Multimodal Learning, by Can Yaras et al.
Explaining and Mitigating the Modality Gap in Contrastive Multimodal Learning
by Can Yaras, Siyi Chen, Peng Wang, Qing Qu
First submitted to arxiv on: 10 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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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 delves into the mechanisms underlying multimodal learning, specifically exploring the emergence of a “modality gap” in models like Contrastive Language-Image Pretraining (CLIP). This phenomenon is characterized by different modalities occupying distinct regions within the shared representation space. By analyzing gradient flow learning dynamics and identifying key factors such as mismatched data pairs and learnable temperature parameters, researchers provide insights for mitigating the modality gap. Strategies include adjusting temperature scheduling and implementing modality swapping. These findings have implications for improving performance on tasks like image-text retrieval. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Multimodal learning is a new way that computers can understand different types of information, like pictures and words. It’s like a special tool that helps machines learn from different sources of data. But researchers found that sometimes these tools don’t work as well as they should because they have trouble understanding the differences between pictures and words. They studied how these tools learn and figured out what makes them get stuck. Now, they can use this knowledge to make the tools better so they can understand more things. |
Keywords
» Artificial intelligence » Pretraining » Temperature
