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Pre-trained Vision-Language (V-L) models set the benchmark for generalizationto downstream tasks among the noteworthy contenders. Many characteristics ofthe V-L model have been explored in existing research including the challengeof the sensitivity to text input and the tuning process across multi-modalprompts. With the advanced utilization of the V-L model like CLIP, recentapproaches deploy learnable prompts instead of hand-craft prompts to boost thegeneralization performance and address the aforementioned challenges. Inspiredby layer-wise training, which is wildly used in image fusion, we note thatusing a sequential training process to adapt different modalities branches ofCLIP efficiently facilitates the improvement of generalization. In the contextof addressing the multi-modal prompting challenge, we propose Token-wiseAdaptive for Multi-modal Prompt Learning (APLe) for tuning both modalitiesprompts, vision and language, as tokens in a sequential manner. APLe addressesthe challenges in V-L models to promote prompt learning across both modalities,which indicates a competitive generalization performance in line with thestate-of-the-art. Preeminently, APLe shows robustness and favourableperformance in prompt-length experiments with an absolute advantage in adoptingthe V-L models.

APLe: Token-Wise Adaptive for Multi-Modal Prompt Learning