AmPLe: Enhancing Vision-Language Models with Adaptive Ensemble Prompting

This research explores a novel approach to improving Vision-Language Models (VLMs) by employing adaptive and debiased ensemble multi-prompt learning. The focus on adaptive techniques and debiasing suggests an effort to overcome limitations in current VLM performance and address potential biases.

• •Proposes a new methodology called AmPLe for improving VLMs.
• •Utilizes adaptive and debiased ensemble multi-prompt learning.
• •Focuses on mitigating biases within existing VLM architectures.