提出了Diversity-Aware Meta Visual Proximating ~（DAM-VP），它是一种高效的提示方法，用于将预先训练好的模型转移到具有冻结主干的下游任务中。视觉提示中的一个具有挑战性的问题是，图像数据集有时具有很大的数据多样性，而每个数据集的通用提示很难处理复杂的分布偏移向原始预训练数据分布正确。为了解决这个问题，我们提出了一个数据集的多样性意识的提示策略，其初始化是由一个元提示。具体来说，我们以多样性自适应的方式将下游数据集聚类成小的同质子集，每个子集都有自己的提示单独优化。这种分而治之的设计大大降低了优化的难度，并显着提高了提示性能。此外，所有提示都使用元提示进行初始化，该元提示是在多个数据集上学习的。这是一个自举的范例，关键的观察是，从以前的数据集学习的提示知识可以帮助提示更快地收敛，并在新的数据集上表现得更好。在推理过程中，我们根据输入和每个子集之间的特征距离为每个输入动态选择适当的提示。通过大量的实验，我们的DAM-VP表现出了卓越的效率和有效性，在不同预训练模型的一系列下游数据集中明显超过了以前的提示方法。我们的代码可从以下网址获得：\url{https://github.com/shikiw/DAM-VP}。
英文摘要：
We present Diversity-Aware Meta Visual Prompting~(DAM-VP), an efficient and effective prompting method for transferring pre-trained models to downstream tasks with frozen backbone. A challenging issue in visual prompting is that image datasets sometimes have a large data diversity whereas a per-dataset generic prompt can hardly handle the complex distribution shift toward the original pretraining data distribution properly. To address this issue, we propose a dataset Diversity-Aware prompting strategy whose initialization is realized by a Meta-prompt. Specifically, we cluster the downstream dataset into small homogeneity subsets in a diversity-adaptive way, with each subset has its own prompt optimized separately. Such a divide-and-conquer design reduces the optimization difficulty greatly and significantly boosts the prompting performance. Furthermore, all the prompts are initialized with a meta-prompt, which is learned across several datasets. It is a bootstrapped paradigm, with the key observation that the prompting knowledge learned from previous datasets could help the prompt to converge faster and perform better on a new dataset. During inference, we dynamically select a proper prompt for each input, based on the feature distance between the input and each subset. Through extensive experiments, our DAM-VP demonstrates superior efficiency and effectiveness, clearly surpassing previous prompting methods in a series of downstream datasets for different pretraining models. Our code is available at: \url{https://github.com/shikiw/DAM-VP}.
Prompt as Free Lunch: Enhancing Diversity in Source-Free Cross-domain Few-shot Learning through Semantic-Guided Prompting
College of Computer and Data Science, Fuzhou University（福州大学计算机与数据科学学院）；Zhejiang University of Technology（浙江工业大学）；Hangzhou International Innovation Institute, Beihang University（北京航空航天大学杭州国际创新研究院）；INSAIT, Sofia University（保加利亚索菲亚大学INSAIT研究院）
Self-regulating Prompts: Foundational Model Adaptation without Forgetting
Mohamed bin Zayed University of AI（穆罕默德·本·扎耶德人工智能大学）；Australian National University（澳大利亚国立大学）；University of California, Merced（加州大学默塞德分校）；Google Research（谷歌研究院）；Linköping University（林雪平大学）
Beyond Templates: Revisiting Zero-Shot Remote Sensing through Meta-Prompting
Remote Sensing Lab, National Technical University of Athens（遥感实验室，国家技术大学雅典分校）；Remote Sensing Lab, Department of Engineering and Sciences, National Technical University of Athens（遥感实验室，工程与科学系，国家技术大学雅典分校）；Universitas Mercatorum（默卡托大学）
Meta-Prompting for Automating Zero-shot Visual Recognition with LLMs
ICG, TU Graz（格拉茨工业大学ICG研究所）；CDL-EML（CDL-EML中心）；MIT-IBM Watson AI Lab（麻省理工学院-IBM沃森人工智能实验室）；JKU（约翰内斯开普勒大学）；IBM Research（IBM研究院）；Weizmann Institute of Science（魏茨曼科学研究所）；University of Bonn（波恩大学）
Institute of Software Chinese Academy of Sciences（中国科学院软件研究所）；Tsinghua University（清华大学）；the Hong Kong University of Science and Technology（香港科技大学）；University of Chinese Academy of Sciences（中国科学院大学）
Prompting Decision Transformer for Few-Shot Policy Generalization
Carnegie Mellon University（卡内基梅隆大学）；University of Montreal（蒙特利尔大学）；Mila（米拉）；MIT-IBM Watson AI Lab（MIT-IBM沃森人工智能实验室）；Massachusetts Institute of Technology（麻省理工学院）；UMass Amherst（马萨诸塞大学阿默斯特分校）
