论文标题
相关衣服：检测人与衣服之间的视觉关系
Relatable Clothing: Detecting Visual Relationships between People and Clothing
论文作者
论文摘要
在计算机视觉和生物识别技术领域，发现人与衣服之间的视觉关系一直是一个相对尚未探索的问题。缺乏用于``磨损''和``未磨损''分类的公共数据集降低了解决此问题的解决方案的开发。我们介绍了相关服装数据集的发布，其中包含35287个人透明的对和分割面具，以开发``磨损''和````dern'''''''''''''''''''''''''''''''''''''''和`此外，我们提出了一个新颖的软关注单元，用于使用深层神经网络进行``磨损''和``未磨损''分类。拟议的软关注模型的准确性为$ 98.55 \％\ pm pm 0.35 \％$ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $可证明高概括性，使我们能够对诸如“磨损”或``undofornorn''的高可见度背心（例如高可见度背心）进行分类。
Detecting visual relationships between people and clothing in an image has been a relatively unexplored problem in the field of computer vision and biometrics. The lack readily available public dataset for ``worn'' and ``unworn'' classification has slowed the development of solutions for this problem. We present the release of the Relatable Clothing Dataset which contains 35287 person-clothing pairs and segmentation masks for the development of ``worn'' and ``unworn'' classification models. Additionally, we propose a novel soft attention unit for performing ``worn'' and ``unworn'' classification using deep neural networks. The proposed soft attention models have an accuracy of upward $98.55\% \pm 0.35\%$ on the Relatable Clothing Dataset and demonstrate high generalizable, allowing us to classify unseen articles of clothing such as high visibility vests as ``worn'' or ``unworn''.
