2 research outputs found
A Global-Local Emebdding Module for Fashion Landmark Detection
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.Comment: Accepted to ICCV 2019 Workshop: Computer Vision for Fashion, Art and
Desig
Fashion Meets Computer Vision: A Survey
Fashion is the way we present ourselves to the world and has become one of
the world's largest industries. Fashion, mainly conveyed by vision, has thus
attracted much attention from computer vision researchers in recent years.
Given the rapid development, this paper provides a comprehensive survey of more
than 200 major fashion-related works covering four main aspects for enabling
intelligent fashion: (1) Fashion detection includes landmark detection, fashion
parsing, and item retrieval, (2) Fashion analysis contains attribute
recognition, style learning, and popularity prediction, (3) Fashion synthesis
involves style transfer, pose transformation, and physical simulation, and (4)
Fashion recommendation comprises fashion compatibility, outfit matching, and
hairstyle suggestion. For each task, the benchmark datasets and the evaluation
protocols are summarized. Furthermore, we highlight promising directions for
future research.Comment: Accepted by ACM Computing Surveys (2021). 39 pages including 2 pages
of supplementary materials and 7 pages of referenc