arXiv:2404.10838v1 Announce Type: cross
Abstract: 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.
Analysis of the Content:
The content of this article focuses on the development of a novel approach to address the challenges of deploying pre-trained multimodal large models in resource-limited environments. The authors propose a dynamic self-adaptive multiscale distillation method that allows for efficient cross-modal representation learning.
One key aspect of this method is the use of a multiscale perspective, which enables the extraction of structural knowledge from the pre-trained multimodal large model. This means that the student model, which is the model being trained, inherits a comprehensive and nuanced understanding of the teacher knowledge. This is crucial for ensuring that the student model maintains high performance.
To optimize the distillation process, the authors propose a dynamic self-adaptive distillation loss balancer. This component eliminates the need for manual loss weight adjustments and dynamically balances each loss item during the distillation process. This not only streamlines the training process but also reduces the computational resources required.
The article highlights that this approach is well-suited for various applications and allows for the deployment of advanced multimodal technologies even in resource-limited settings. This is particularly relevant in fields such as multimedia information systems, animations, artificial reality, augmented reality, and virtual realities, where computational resources can be a limiting factor.
The authors also mention that their approach achieves state-of-the-art performance on cross-modal retrieval tasks using only image-level information. This is notable because previous methods relied on region-level information, which requires more computational resources.
Expert Insights:
The proposed approach in this article is highly significant for the field of multimedia information systems and related areas such as animations, artificial reality, augmented reality, and virtual realities. These fields often involve the processing and analysis of multimodal data, such as images and text, and require efficient representation learning methods.
The multiscale perspective employed in this approach is particularly interesting from a multidisciplinary standpoint. It combines concepts from computer vision, natural language processing, and knowledge distillation to enhance the learning process. This integration of different disciplines allows for a more comprehensive understanding of the data and improves the performance of the trained models.
The dynamic self-adaptive distillation loss balancer is another innovative component of this approach. Manual adjustments of loss weights can be time-consuming and may not lead to optimal results. By automating this process and dynamically balancing the loss items, the training becomes more efficient and effective. This is crucial in resource-limited environments, where computational resources are scarce.
The findings of this study not only contribute to the field of multimodal representation learning but also have practical implications. The ability to deploy advanced multimodal technologies in resource-limited settings opens up new possibilities for various applications. For example, in the field of augmented reality, where computational resources are often limited on mobile devices, this approach could enable more sophisticated and interactive AR experiences.
Overall, this article provides valuable insights into the development of efficient cross-modal representation learning methods and their applicability in multimedia information systems and related fields. The combination of the multiscale perspective and dynamic self-adaptive distillation loss balancer makes this approach highly promising for future research and practical implementations.
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