This paradigm encapsulates knowledge from various models into a solitary prompt without altering the original models or requiring access to the training data, which enables us to achieve efficient and convenient knowledge transfer in more realistic scenarios. From a practicality standpoint, this paradigm not only for the first time proves the effectiveness of Visual Prompt in data inaccessible contexts, but also solves the problems of low model reusability and high storage resource consumption faced by traditional Data-Free Knowledge Transfer, which means that we can realize the parallel knowledge transfer of multiple models without modifying any source model. Extensive experiments across various datasets and models demonstrate the efficacy of the proposed KiOP knowledge transfer paradigm. Without access to real training data and with rigorous storage capacity constraints, it is also capable of yielding considerable outcomes when dealing with cross-model backbone setups and handling parallel knowledge transfer processing requests with multiple (more than 2) models.

该研究提出了一种名为KiOP的知识转移范式，通过将各种模型的知识封装到一个单独的提示中，实现了在更现实的场景中实现高效、方便的知识转移。该范式不仅证明了Visual Prompt在无法访问数据的情况下的有效性，而且解决了传统无数据知识转移面临的低模型可重用性和高存储资源消耗问题，因此能够在不修改任何源模型的情况下实现多个模型的并行知识转移。通过在各种数据集和模型上进行的广泛实验，验证了所提出的KiOP知识转移范式的有效性，即使在没有真实训练数据的情况下，也能在处理多个模型的交叉骨干设置和并行知识转移处理请求（超过2个模型）时产生可观的结果。

一个提示封装知识的方法