Robotics & Machine Learning Daily News2024,Issue(Jun.7) :127-128.

Research from Wenzhou University Reveals New Findings on Artificial Intelligence (Artificial Intelligence Enabled Apparel Design Research)

温州大学的研究揭示了人工智能的新发现(人工智能辅助服装设计研究)

Robotics & Machine Learning Daily News2024,Issue(Jun.7) :127-128.

Research from Wenzhou University Reveals New Findings on Artificial Intelligence (Artificial Intelligence Enabled Apparel Design Research)

温州大学的研究揭示了人工智能的新发现(人工智能辅助服装设计研究)

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摘要

由一名新闻记者兼机器人与机器学习每日新闻编辑每日新闻-关于人工智能ce的详细数据已经呈现。根据NewsRx编辑的《浙江人民日报》报道,这项研究称,“随着对个性化时尚需求的增加,传统的服装设计方法很难跟上市场的期望。”我们的新闻记者从温州大学的研究中获得了一句话:“本研究探讨了人工智能如何增强服装设计,从而创造出一个与创新服装设计发展轨迹同步的数字化定制过程。本研究将Deeplabv3+模型与交叉注意机制相结合,开发了一个适合服装设计的图像分割网络。”摘要:为了扩大设计形式的多样性,引入wgan-gp模型对服装色彩设计进行动态优化,保证设计与用户偏好一致,验证了这些人工智能技术在服装设计中的有效性。分别对设计分割和颜色优化进行了仿真验证。结果表明,在val idation数据集上,Deeplabv3+网络的平均相交比Union(MioU)提高了6.97%,比OCRNet平均提高了2.28个百分点。

Abstract

By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Data detailed on artificial intelligen ce have been presented. According to news originating from Zhejiang, People’s Re public of China, by NewsRx editors, the research stated, “With the increasing de mand for personalized fashion, the conventional approach to clothing design stru ggles to keep up with market expectations.” Our news correspondents obtained a quote from the research from Wenzhou Universi ty: “This study explores how artificial intelligence can enhance clothing design , resulting in the creation of a digital customization process that is in step w ith the evolving trajectory of innovative fashion design. This study integrates the Deeplabv3+ model with a cross-cutting attention mechanism to develop a novel image segmentation network tailored for clothing design, aiming to expand the d iversity of design forms. Additionally, the WGAN-GP model is introduced for adap tive optimization of clothing color design, ensuring that the designs align with user preferences. To verify the efficacy of these AI technologies in apparel de sign, separate simulation verifications for design segmentation and color optimi zation were conducted. The results show that the Deeplabv3+ network achieved a 6 .97% improvement in Mean Intersection over Union (MioU) on the val idation dataset, outperforming the OCRNet average by 2.28 percentage points.”

Key words

Wenzhou University/Zhejiang/People’s R epublic of China/Asia/Artificial Intelligence/Emerging Technologies/Machine Learning

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出版年

2024
Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

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