用于高效动作分类的双模型蒸馏与混合边云解决方案
Dual-Model Distillation for Efficient Action Classification with Hybrid Edge-Cloud Solution
AI总结：
针对大模型边缘部署成本高的问题，本文提出双模型蒸馏（DMD）无监督数据生成方法训练轻量切换器，在边缘模型不确定时选择性卸载至云端大模型，实现动作分类中更低计算开销与更高准确率。
摘要：
随着大型视频语言模型（VLMs）等人工智能模型规模不断扩大，受硬件限制和计算成本影响，它们在实际应用中的部署变得越来越困难。为解决这一问题，我们设计了一种混合边云解决方案，利用较小模型的高效性进行本地处理，并在必要时将推理任务交由更大、更准确的云端模型完成。具体而言，我们提出了一种新颖的无监督数据生成方法，称为双模型蒸馏（Dual-Model Distillation，DMD），用于训练一个轻量级切换器模型，该模型能够预测边缘模型输出何时存在不确定性，并选择性地将推理卸载到云端的大型模型。在动作分类任务上的实验结果表明，与单独使用大型模型相比，我们的框架不仅计算开销更低，而且准确率更高。我们的框架为资源受限环境下的动作分类提供了一种可扩展且可适应的解决方案，并具有超越医疗健康领域的潜在应用前景。值得注意的是，虽然DMD生成的数据在我们的流程中被用于优化性能和资源使用，但我们期望DMD这一概念能够进一步支持未来关于多模型之间知识对齐的研究。
英文摘要：
As Artificial Intelligence models, such as Large Video-Language models (VLMs), grow in size, their deployment in real-world applications becomes increasingly challenging due to hardware limitations and computational costs. To address this, we design a hybrid edge-cloud solution that leverages the efficiency of smaller models for local processing while deferring to larger, more accurate cloud-based models when necessary. Specifically, we propose a novel unsupervised data generation method, Dual-Model Distillation (DMD), to train a lightweight switcher model that can predict when the edge model's output is uncertain and selectively offload inference to the large model in the cloud. Experimental results on the action classification task show that our framework not only requires less computational overhead, but also improves accuracy compared to using a large model alone. Our framework provides a scalable and adaptable solution for action classification in resource-constrained environments, with potential applications beyond healthcare. Noteworthy, while DMD-generated data is used for optimizing performance and resource usage in our pipeline, we expect the concept of DMD to further support future research on knowledge alignment across multiple models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
