Summary of Enabling Small Models For Zero-shot Selection and Reuse Through Model Label Learning, by Jia Zhang et al.
Enabling Small Models for Zero-Shot Selection and Reuse through Model Label Learning
by Jia Zhang, Zhi Zhou, Lan-Zhe Guo, Yu-Feng Li
First submitted to arxiv on: 21 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 Vision-language models (VLMs) like CLIP have achieved remarkable zero-shot performance in image classification tasks by aligning text and images, but struggle to match the performance of task-specific expert models. Conversely, expert models excel in their specialized domains but lack zero-shot ability for new tasks. To bridge this gap, we propose a novel paradigm, Model Label Learning (MLL), which constructs a model hub and aligns models with their functionalities using model labels. MLL leverages a Semantic Directed Acyclic Graph (SDAG) and an algorithm, Classification Head Combination Optimization (CHCO), to select capable models for new tasks. Compared to the foundation model paradigm, MLL is less costly and more scalable, allowing zero-shot ability to grow with the size of the model hub. Our experiments on seven real-world datasets validate the effectiveness and efficiency of MLL, demonstrating that expert models can be effectively reused for zero-shot tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Researchers have developed special computer programs called vision-language models (VLMs) that are good at recognizing images when given words to describe them. However, these VLMs aren’t as good as other specialized programs that are only trained to recognize specific types of images. The goal is to create a program that combines the strengths of both types. We propose a new way to do this by grouping similar programs together and giving each one a label describing what it’s good at. This allows us to use these programs to recognize new types of images without needing to train them from scratch. Our results show that this approach is effective and efficient, allowing us to reuse old programs for new tasks. |
Keywords
» Artificial intelligence » Classification » Image classification » Optimization » Zero shot
