DL-EWF：利用基于 Grounded-Segment-Anything 分割的深度学习赋能女性时尚，以进行体型分类
DL-EWF: Deep Learning Empowering Women's Fashion with Grounded-Segment-Anything Segmentation for Body Shape Classification
AI总结：
针对女性体型与服装不匹配问题，本文提出结合 Grounded-Segment-Anything 分割与多种预训练 DNN 的体型分类框架，在 Style4BodyShape 五分类任务中验证 Inception V3 效果最佳。
摘要：
全球时尚产业在全球经济中发挥着关键作用，解决该产业中的基础性问题对于开发创新解决方案至关重要。时尚产业最紧迫的挑战之一是个人体型与其所购买服装之间的不匹配。这一问题在非理想体型人群中尤为普遍，并加剧了他们所面临的困难。考虑到体型在个体之间的差异，设计和生产能够被消费者广泛接受的服装是必不可少的。用于确定人体体型的传统方法存在局限性，原因在于其准确率低、成本高且耗时长。利用数字成像和深度神经网络（DNN）的新方法已被引入，用于识别人体体型。在本研究中，使用 Style4BodyShape 数据集将体型分为五类：矩形、三角形、倒三角形、沙漏形和苹果形。本文从图像中提取人物的体型分割结果，忽略周围环境和背景。随后，使用多种预训练模型（如 ResNet18、ResNet34、ResNet50、VGG16、VGG19 和 Inception v3）对分割结果进行分类。在这些预训练模型中，Inception V3 模型在 f1-score 评价指标和准确率方面表现出优于其他模型的性能。
英文摘要：
The global fashion industry plays a pivotal role in the global economy, and addressing fundamental issues within the industry is crucial for developing innovative solutions. One of the most pressing challenges in the fashion industry is the mismatch between body shapes and the garments of individuals they purchase. This issue is particularly prevalent among individuals with non-ideal body shapes, exacerbating the challenges faced. Considering inter-individual variability in body shapes is essential for designing and producing garments that are widely accepted by consumers. Traditional methods for determining human body shape are limited due to their low accuracy, high costs, and time-consuming nature. New approaches, utilizing digital imaging and deep neural networks (DNN), have been introduced to identify human body shape. In this study, the Style4BodyShape dataset is used for classifying body shapes into five categories: Rectangle, Triangle, Inverted Triangle, Hourglass, and Apple. In this paper, the body shape segmentation of a person is extracted from the image, disregarding the surroundings and background. Then, Various pre-trained models, such as ResNet18, ResNet34, ResNet50, VGG16, VGG19, and Inception v3, are used to classify the segmentation results. Among these pre-trained models, the Inception V3 model demonstrates superior performance regarding f1-score evaluation metric and accuracy compared to the other 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。
