  In recent years, pre-trained multimodal large models have attracted
widespread attention due to their outstanding performance in various multimodal
applications. Nonetheless, the extensive computational resources and vast
datasets required for their training present significant hurdles for deployment
in environments with limited computational resources. To address this
challenge, we propose a novel dynamic self-adaptive multiscale distillation
from pre-trained multimodal large model for efficient cross-modal
representation learning for the first time. Unlike existing distillation
methods, our strategy employs a multiscale perspective, enabling the extraction
structural knowledge across from the pre-trained multimodal large model.
Ensuring that the student model inherits a comprehensive and nuanced
understanding of the teacher knowledge. To optimize each distillation loss in a
balanced and efficient manner, we propose a dynamic self-adaptive distillation
loss balancer, a novel component eliminating the need for manual loss weight
adjustments and dynamically balances each loss item during the distillation
process. Our methodology streamlines pre-trained multimodal large models using
only their output features and original image-level information, requiring
minimal computational resources. This efficient approach is suited for various
applications and allows the deployment of advanced multimodal technologies even
in resource-limited settings. Extensive experiments has demonstrated that our
method maintains high performance while significantly reducing model complexity
and training costs. Moreover, our distilled student model utilizes only
image-level information to achieve state-of-the-art performance on cross-modal
retrieval tasks, surpassing previous methods that relied on region-level
information.
