Summary of Tuning Vision-language Models with Candidate Labels by Prompt Alignment, By Zhifang Zhang et al.
Tuning Vision-Language Models with Candidate Labels by Prompt Alignment
by Zhifang Zhang, Yuwei Niu, Xin Liu, Beibei Li
First submitted to arxiv on: 10 Jul 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 explores the capabilities of vision-language models (VLMs) in fine-tuning tasks, specifically when working with limited and noisy label data. Current approaches, such as prompt learning, are effective but require labeled datasets. The authors propose a novel framework to handle candidate labels, where true labels are not available due to data privacy concerns. The framework leverages the VLM’s prior knowledge by aligning model outputs with mixed class posteriors predicted by both learnable and handcrafted prompts. Experimental results demonstrate the effectiveness of this approach in improving performance when dealing with ambiguous candidate labels. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a computer that can understand images and words together. It’s called a vision-language model (VLM). Right now, we need lots of labeled data to teach VLMs new tasks. But what if we don’t have enough labeled data? That’s where this paper comes in. The authors want to find a way for VLMs to learn even when the labels are incomplete or unclear. They propose a new approach that helps VLMs make better decisions by combining its own knowledge with some extra information. This method can improve performance and work well even when we don’t have perfect labels. |
Keywords
» Artificial intelligence » Fine tuning » Language model » Prompt
