Summary of Candidate Pseudolabel Learning: Enhancing Vision-language Models by Prompt Tuning with Unlabeled Data, By Jiahan Zhang et al.
Candidate Pseudolabel Learning: Enhancing Vision-Language Models by Prompt Tuning with Unlabeled Data
by Jiahan Zhang, Qi Wei, Feng Liu, Lei Feng
First submitted to arxiv on: 15 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 In this paper, researchers tackle the challenge of fine-tuning vision-language models (VLMs) using unlabeled data. They propose a Candidate Pseudolabel Learning method to generate suitable candidate pseudolabels for downstream tasks. The approach uses a confidence score matrix to select refined candidate pseudolabels that improve label inclusion and class-balanced instance selection. The authors demonstrate the effectiveness of their method on nine benchmark datasets using three learning paradigms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary VLMs are being fine-tuned with unlabeled data, which is important because it helps them learn from a wide range of sources without needing labeled examples. One problem with this approach is that some of these models don’t perform well in certain tasks right away. To solve this issue, the authors came up with a new method to generate better candidate pseudolabels for unlabeled data. These pseudolabels are like guesses about what labels should be assigned to each piece of data. The authors used a special kind of score to help them pick the best pseudolabels and make sure they were balanced, meaning not favoring one class over another. They tested their method on lots of different datasets and it worked really well. |
Keywords
» Artificial intelligence » Fine tuning
