Diversity-Aware Meta Visual Prompting (2303.08138v1)

Abstract: 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}.

An Expert Overview of "Diversity-Aware Meta Visual Prompting"

The paper "Diversity-Aware Meta Visual Prompting" addresses a significant challenge in the field of transferring pre-trained visual models to downstream tasks, specifically when dealing with datasets characterized by substantial diversity. This paper introduces a novel prompting mechanism, Diversity-Aware Meta Visual Prompting (DAM-VP), designed to maintain the backbone of pre-trained models fixed while effectively managing the intricacies of diverse data distributions during task transfer.

Core Methodology and Results

DAM-VP implements a two-pronged strategy to strengthen visual prompting. It begins with a diversity-aware dataset partitioning, which ingeniously clusters the downstream dataset into subsets of data with relative homogeneity, allowing each subset to be optimized with its own specific prompt. This divide-and-conquer approach not only alleviates the complexity of prompt optimization but also significantly enhances performance across diverse datasets.

Furthermore, DAM-VP utilizes a meta-learning framework to initialize prompts. A meta-prompt is extracted from multiple datasets, serving as the initialization point for optimizing prompts on new tasks. This meta-prompt leverages learned experience from prior datasets to expedite convergence and improve downstream performance, showcasing remarkable adaptability.

Empirical evaluations confirm the efficacy of DAM-VP, as it consistently surpasses existing prompting techniques across various datasets with different pre-trained models, including ViT-B/16 and Swin-Base. Notably, with the ImageNet-22k pre-trained ViT-B model, DAM-VP achieved a remarkable top-1 accuracy of 73.1% on the DTD dataset, notably outstripping Visual Prompting (VP) and Visual Prompt Tuning (VPT) techniques by significant margins.

Implications and Future Directions

The implications of DAM-VP are multidimensional. Practically, it offers an efficient mechanism for adapting large-scale vision models to an array of downstream tasks without the need for fully retraining the models for each task. This enhances usability in applications where storage and computational resources are constraints, encouraging broader adoption of state-of-the-art model architectures in real-time applications.

Theoretically, DAM-VP presents a promising direction for addressing domain shift in transfer learning. By systematically clustering data and employing meta-learning for prompt initialization, it provides a structured strategy to understand and leverage diverse data distributions, thereby potentially informing future architectures that can autonomously adjust to data diversity.

Looking forward, DAM-VP paves the way for further advancements in visual prompting, particularly in developing frameworks that can intuitively discern data characteristics and adaptively scale parameters for optimal task performance. Exploring its adaptability to non-visual modalities might extend its utility to a broader AI landscape, potentially influencing the design of multi-modal prompting frameworks and even fostering integration with other AI subfields like NLP.

Conclusion

"Diversity-Aware Meta Visual Prompting" represents a noteworthy contribution to the field of visual model adaptation, advancing our understanding of how diversity in data can be systematically harnessed to boost task performance. By seamlessly integrating clustering strategies with meta-learning, DAM-VP sets a new benchmark in the quest for efficient and robust visual prompting methods, promising a more adaptive and resource-efficient pathway to leveraging pre-trained models across varied applications.