Summary of Aapl: Adding Attributes to Prompt Learning For Vision-language Models, by Gahyeon Kim et al.
AAPL: Adding Attributes to Prompt Learning for Vision-Language Models
by Gahyeon Kim, Sohee Kim, Seokju Lee
First submitted to arxiv on: 25 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 The paper proposes a novel mechanism called “Adding Attributes to Prompt Learning” (AAPL) that aims to improve the performance of prompt learning on zero-shot downstream tasks. By disentangling low-level visual augmentation features from high-level class information, AAPL guides the learnable context to effectively extract text features for unseen classes. The authors demonstrate the effectiveness of AAPL across 11 datasets, achieving favorable performances in few-shot learning, zero-shot learning, cross-dataset, and domain generalization tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about making computers better at understanding pictures and words together. Right now, computers are pretty good at doing this if they’re shown a lot of examples beforehand, but they’re not as good when they haven’t seen the pictures or words before. The authors came up with a new way to help computers learn more by “adding attributes” to the learning process. This new method is called AAPL and it helps computers understand text better, especially when they don’t have any examples to go off of. |
Keywords
» Artificial intelligence » Domain generalization » Few shot » Prompt » Zero shot
