Summary of Towards Modality Generalization: a Benchmark and Prospective Analysis, by Xiaohao Liu et al.
Towards Modality Generalization: A Benchmark and Prospective Analysis
by Xiaohao Liu, Xiaobo Xia, Zhuo Huang, Tat-Seng Chua
First submitted to arxiv on: 24 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 Modality Generalization (MG) to enable machine learning models to generalize to novel modalities that are not present during training, addressing the current limitations of multi-modal learning methods. The proposed approach focuses on two cases: weak MG, where existing perceptors can be used to map seen and unseen modalities into a joint embedding space, and strong MG, where no such mappings exist. A comprehensive benchmark featuring multi-modal algorithms is also introduced to facilitate progress in this area. Extensive experiments demonstrate the complexity of MG and identify key directions for future research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper wants to help machines learn from many different kinds of data at once. Right now, they do a great job when the types of data they’re learning from are ones they’ve seen before. But what if new types of data come along that they haven’t seen before? That’s where this idea called Modality Generalization comes in. It’s about teaching machines to learn from these new types of data even if they’ve never seen them before. The researchers propose a way to do this and test it out with some experiments. Their results show that there is still more work to be done, but it’s an important step towards making machines smarter. |
Keywords
» Artificial intelligence » Embedding space » Generalization » Machine learning » Multi modal
