标题： Swin-TUNA：一种用于精确食品图像分割的新型PEFT方法 显示英文标题

标题： Swin-TUNA : A Novel PEFT Approach for Accurate Food Image Segmentation

摘要： 在食品图像处理领域，高效的语义分割技术对于工业应用至关重要。 然而，现有的大规模基于Transformer的模型（如FoodSAM）由于参数量庞大和计算资源需求高，在满足实际部署要求方面面临挑战。 本文介绍了TUNable Adapter模块（Swin-TUNA），这是一种参数高效的微调（PEFT）方法，将多尺度可训练适配器集成到Swin Transformer架构中，仅更新4%的参数即可实现高性能的食品图像分割。 Swin-TUNA的核心创新在于其分层特征适应机制：它在深度和不同尺度的维度映射中设计了可分离卷积，以解决浅层和深层网络之间特征的差异，并结合任务无关和任务特定特征的动态平衡策略。 实验表明，该方法在FoodSeg103和UECFoodPix Complete数据集上的mIoU分别达到50.56%和74.94%，超越了完全参数化的FoodSAM模型，同时将参数量减少了98.7%（仅剩8.13M）。 此外，Swin-TUNA在低数据场景下表现出更快的收敛速度和更强的泛化能力，为构建轻量级食品图像提供了高效的解决方案。 显示英文摘要

摘要： In the field of food image processing, efficient semantic segmentation techniques are crucial for industrial applications. However, existing large-scale Transformer-based models (such as FoodSAM) face challenges in meeting practical deploymentrequirements due to their massive parameter counts and high computational resource demands. This paper introduces TUNable Adapter module (Swin-TUNA), a Parameter Efficient Fine-Tuning (PEFT) method that integrates multiscale trainable adapters into the Swin Transformer architecture, achieving high-performance food image segmentation by updating only 4% of the parameters. The core innovation of Swin-TUNA lies in its hierarchical feature adaptation mechanism: it designs separable convolutions in depth and dimensional mappings of varying scales to address the differences in features between shallow and deep networks, combined with a dynamic balancing strategy for tasks-agnostic and task-specific features. Experiments demonstrate that this method achieves mIoU of 50.56% and 74.94% on the FoodSeg103 and UECFoodPix Complete datasets, respectively, surpassing the fully parameterized FoodSAM model while reducing the parameter count by 98.7% (to only 8.13M). Furthermore, Swin-TUNA exhibits faster convergence and stronger generalization capabilities in low-data scenarios, providing an efficient solution for assembling lightweight food image.