Detecting fashion landmarks is a fundamental technique for visual clothing
analysis. Due to the large variation and non-rigid deformation of clothes,
localizing fashion landmarks suffers from large spatial variances across poses,
scales, and styles. Therefore, understanding contextual knowledge of clothes is
required for accurate landmark detection. To that end, in this paper, we
propose a fashion landmark detection network with a global-local embedding
module. The global-local embedding module is based on a non-local operation for
capturing long-range dependencies and a subsequent convolution operation for
adopting local neighborhood relations. With this processing, the network can
consider both global and local contextual knowledge for a clothing image. We
demonstrate that our proposed method has an excellent ability to learn advanced
deep feature representations for fashion landmark detection. Experimental
results on two benchmark datasets show that the proposed network outperforms
the state-of-the-art methods. Our code is available at
this https URL

本文介绍了一种基于全局 - 局部嵌入模块的时尚地标检测网络，能够考虑服装图像的全局和局部相关知识，其中包括非局部操作用于捕捉远距离依赖关系和接下来的卷积操作用于采用局部邻域关系。实验结果表明，该方法在时尚地标检测方面有着很好的能力，并且优于最先进的方法.

全局 - 局部嵌入式模块用于时尚关键点检测

A Global-Local Emebdding Module for Fashion Landmark Detection

Fashion landmarks are functional key points defined on clothes, such as
corners of neckline, hemline, and cuff. They have been recently introduced as
an effective visual representation for fashion image understanding. However,
detecting fashion landmarks are challenging due to background clutters, human
poses, and scales. To remove the above variations, previous works usually
assumed bounding boxes of clothes are provided in training and test as
additional annotations, which are expensive to obtain and inapplicable in
practice. This work addresses unconstrained fashion landmark detection, where
clothing bounding boxes are not provided in both training and test. To this
end, we present a novel Deep LAndmark Network (DLAN), where bounding boxes and
landmarks are jointly estimated and trained iteratively in an end-to-end
manner. DLAN contains two dedicated modules, including a Selective Dilated
Convolution for handling scale discrepancies, and a Hierarchical Recurrent
Spatial Transformer for handling background clutters. To evaluate DLAN, we
present a large-scale fashion landmark dataset, namely Unconstrained Landmark
Database (ULD), consisting of 30K images. Statistics show that ULD is more
challenging than existing datasets in terms of image scales, background
clutters, and human poses. Extensive experiments demonstrate the effectiveness
of DLAN over the state-of-the-art methods. DLAN also exhibits excellent
generalization across different clothing categories and modalities, making it
extremely suitable for real-world fashion analysis.

本研究针对传统依赖昂贵的边框注释来减少不确定性的困境，提出了 Deep LAndmark Network (DLAN)，通过联合估计边框和关键点，有效解决了服装标记点检测的问题。在大规模的无限制服装里程碑数据库（ULD）上测试表明，DLAN 对不同类型的服装和模式的泛化表现极佳，非常适用于真实世界的时尚分析。