Summary of Improved Few-shot Image Classification Through Multiple-choice Questions, by Dipika Khullar et al.
Improved Few-Shot Image Classification Through Multiple-Choice Questions
by Dipika Khullar, Emmett Goodman, Negin Sokhandan
First submitted to arxiv on: 23 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 Through a simple multiple-choice language prompt, Vision-and-Language (VQA) models can operate as zero-shot image classifiers, producing classification labels. Unlike typical image encoders, VQA models offer an advantage: they can be infused with relevant visual information through tailored language prompts. However, for most tasks, zero-shot VQA performance is lacking due to unfamiliar category names or dissimilar pre-training data and test data distributions. The proposed method boosts VQA performance for image classification using only a handful of labeled examples and multiple-choice questions. This few-shot method is training-free and maintains the dynamic and flexible advantages of VQA models. By extracting prompt-specific latent representations, which are enriched with relevant visual information, and combining them to create a final overall image embedding, decoded via reference to latent class prototypes constructed from the few labeled examples, impressive performance is achieved on common few-shot tasks including MiniImageNet, Caltech-UCSD Birds, and CIFAR-100. The approach outperforms both pure visual encoders and zero-shot VQA baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about how machines can learn to recognize images without being specifically trained for that task. They use a special kind of language prompt to help the machine understand what it’s looking at. Usually, this method doesn’t work very well because the machine might not know what some words mean or have seen similar pictures before. To fix this, the researchers developed a new way to use just a few labeled examples and multiple-choice questions to improve the machine’s image recognition abilities. This approach works better than other methods on certain types of tasks, like recognizing different articles of clothing based on their fabric, texture, and view. |
Keywords
» Artificial intelligence » Classification » Embedding » Few shot » Image classification » Prompt » Zero shot
