Summary of Like Humans to Few-shot Learning Through Knowledge Permeation Of Vision and Text, by Yuyu Jia et al.
Like Humans to Few-Shot Learning through Knowledge Permeation of Vision and Text
by Yuyu Jia, Qing Zhou, Wei Huang, Junyu Gao, Qi Wang
First submitted to arxiv on: 21 May 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 This paper proposes a new method for few-shot learning called BiKop, which combines visual and textual knowledge to recognize novel classes with only a few support samples. Building upon advanced methods that introduce class names as prior knowledge, BiKop establishes a hierarchical joint representation through bidirectional permeation of general and specific information. To alleviate the suppression of novel-class-relevant information, the model disentangles base-class-relevant semantics during training. Experimental results on four challenging benchmarks demonstrate the superiority of BiKop. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary BiKop is a new way to learn about new things with just a few examples. It’s like having a hint or a clue that helps you figure out what something is, even if it looks very different from anything you’ve seen before. The researchers created a special method that combines words and pictures to help the computer understand what it’s looking at. This makes it better than other methods at recognizing new things with just a few examples. |
Keywords
» Artificial intelligence » Few shot » Semantics
