arXiv:2603.09493v2 Announce Type: replace-cross Abstract: The adaptation of large-scale vision-language models (VLMs) to downstream tasks with limited labeled data remains a significant challenge. While parameter-efficient prompt learning methods offer a promising path, they often suffer from catastrophic forgetting of pre-trained knowledge. Toward addressing this limitation, our work is grounded in the insight that governing the evolutionary path of prompts is essential for forgetting-free adaptation. To this end, we propose EvoPrompt, a novel framework designed to explicitly steer the prompt
The paper addresses a current fundamental challenge in adapting large vision-language models efficiently without sacrificing pre-existing knowledge, a critical area in active AI development.
Improving prompt learning for VLMs unlocks more efficient deployment of powerful AI, crucial for widespread adoption and task-specific applications with limited data.
This framework offers a method to evolve prompts in a 'forgetting-free' manner, potentially making VLM adaptation more robust and less resource-intensive.
- · AI developers
- · Companies using VLMs
- · Edge AI applications
- · Open-source AI community
- · Inefficient prompt engineering methods
- · Organizations relying solely on fine-tuning
VLMs become more adaptable and performant on a wider array of downstream tasks with less data.
Accelerated deployment of AI applications across various industries due to reduced adaptation costs and improved model fidelity.
Enhanced competition in applied AI as advanced model capabilities become more accessible and easier to customize.
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Read at arXiv cs.AI