Summary of Cross-modal Few-shot Learning: a Generative Transfer Learning Framework, by Zhengwei Yang et al.
Cross-Modal Few-Shot Learning: a Generative Transfer Learning Framework
by Zhengwei Yang, Yuke Li, Qiang Sun, Basura Fernando, Heng Huang, Zheng Wang
First submitted to arxiv on: 14 Oct 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 introduces Cross-modal Few-Shot Learning (CFSL), a task that recognizes instances across multiple modalities using limited labeled data. The authors propose a Generative Transfer Learning (GTL) framework to address the unique challenges of CFSL, which arise from visual attribute and structural disparities between modalities. GTL jointly estimates latent shared concepts and in-modality disturbances through a generative structure, enabling effective transfer knowledge from unimodal to multimodal data. The authors demonstrate state-of-the-art performance across seven multi-modal datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us learn how machines can recognize things when we show them only a few examples from different sources. For example, if we want a machine to recognize drawings of animals and real-life pictures of animals, it’s hard because they look very different. The authors come up with a new way for the machine to figure out what makes these images similar despite their differences. They test this method on many different kinds of images and show that it works really well. |
Keywords
» Artificial intelligence » Few shot » Multi modal » Transfer learning
