  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
https://github.com/shumming/GLE_FLD.
