 like CLIP have demonstrated impressive zero-shot ability in image classification tasks by aligning text and images but suffer inferior performance compared with task-specific expert models. On the contrary%2C expert models excel in their specialized domains but lack zero-shot ability for new tasks. How to obtain both the high performance of expert models and zero-shot ability is an important research direction. In this paper%2C we attempt to demonstrate that by constructing a model hub and aligning models with their functionalities using model labels%2C new tasks can be solved in a zero-shot manner by effectively selecting and reusing models in the hub. We introduce a novel paradigm%2C Model Label Learning (MLL)%2C which bridges the gap between models and their functionalities through a Semantic Directed Acyclic Graph (SDAG) and leverages an algorithm%2C Classification Head Combination Optimization (CHCO)%2C to select capable models for new tasks. Compared with the foundation model paradigm%2C it is less costly and more scalable%2C i.e.%2C the zero-shot ability grows with the sizes of the model hub. Experiments on seven real-world datasets validate the effectiveness and efficiency of MLL%2C demonstrating that expert models can be effectively reused for zero-shot tasks. Our code will be released publicly.?quality=80&w=1200)
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