Summary of What to Align in Multimodal Contrastive Learning?, by Benoit Dufumier et al.
What to align in multimodal contrastive learning?
by Benoit Dufumier, Javiera Castillo-Navarro, Devis Tuia, Jean-Philippe Thiran
First submitted to arxiv on: 11 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); 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 This paper introduces CoMM, a Contrastive MultiModal learning strategy that enables communication between modalities in a single multimodal space. By maximizing the mutual information between augmented versions of multimodal features, CoMM aligns representations and estimates multimodal interactions beyond redundancy. Theoretical analysis shows that shared, synergistic, and unique terms of information emerge from this formulation. In controlled experiments, CoMM captures redundant, unique, and synergistic information between modalities. In real-world settings, CoMM learns complex multimodal interactions and achieves state-of-the-art results on seven multimodal benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary CoMM is a new way for computers to understand how different senses work together. It’s like having a conversation with someone where you use multiple ways of communicating, like words, tone, and body language. CoMM helps machines learn from all these different types of information and figure out what’s important. It does this by looking at different versions of the same thing and finding the connections between them. This makes it really good at understanding complex interactions between senses. In tests, CoMM did better than other methods on seven different tasks that require understanding multiple senses. |
